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1

Synthesis of Reconfigurable Multiple Shaped Beams of a Concentric Circular Ring Array Antenna Using Evolutionary Algorithms

Dubey Sanjay Kumar, Mandal Debasis

Journal of Telecommunications and Information Technology

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2023

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nr 1

8--17

EN

The approach described in this paper uses evolutionary algorithms to create multiple-beam patterns for a concentric circular ring array (CCRA) of isotropic antennas using a common set of array excitation amplitudes. The flat top, cosec2, and pencil beam patterns are examples of multiple-beam patterns. All of these designs have an upward angle of θ = 0◦. All the patterns are further created in three azimuth planes (φ = 0◦, 5◦, and 10◦). To create the necessary patterns, non-uniform excitations are used in combination with evenly spaced isotropic components. For the flat top and cosecant-squared patterns, the best combination of common components, amplitude and various phases is applied, whereas the pencil beam pattern is produced using the common amplitude only. Differential evolutionary algorithm (DE), genetic algorithm (GA), and firefly algorithm (FA) are used to generate the best 4-bit discrete magnitudes and 5-bit discrete phases. These discrete excitations aid in lowering the feed network design complexity and the dynamic range ratio (DRR). A variety of randomly selected azimuth planes are used to verify the excitations as well. With small modifications in the desired parameters, the patterns are formed using the same excitation. The results proved both the efficacy of the suggested strategy and the dominance of DE over GA as well as FA.

2

QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Doorwar Minaxi, Malathi P

International Journal of Electronics and Telecommunications

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2023

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Vol. 69, No. 4

776--784

EN

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.

3

Switched reluctance motor drives speed control using optimized PID controller

Ibrahim Mohammed A., Alsammak Ahmed Nasser B.

Przegląd Elektrotechniczny

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2022

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R. 98, nr 11

46--50

EN

The switched reluctance motor (SRM) has turned out to be an outstanding resolution for a various appliances. The modern invents of SRM grant consumers to yield advantage of small starting currents , better efficiency and robust structure that illustrates this kind of motor. This article aims at analyzing and modeling the switched reluctance motor speed controller utilizing a Proportional Integral derivative (PID) controller. The non-linear character of the SRM magnetic properties is currently fetched into attention for modeling . These nonlinearities of the switched reluctance machines attain the traditional PID controller an inadequate selection for appliance where high dynamic performance drive is required. Genetic Algorithm (GA) is manipulated to adjust the PID coefficients for the SRM drive. The consequences achieved indicates that the utilization of these established algorithms controller enhances the transient and steady state performances.

PL

Silnik z przełączaną reluktancją (SRM) okazał się być znakomitym rozwiązaniem dla różnych urządzeń. Nowoczesne wynalazki SRM dają konsumentom korzyści w postaci małych prądów rozruchowych, lepszej wydajności i solidnej konstrukcji, która ilustruje ten rodzaj silnika. Celem artykułu jest analiza i modelowanie regulatora prędkości silnika z przełączaną reluktancją, wykorzystującego regulator proporcjonalnocałkująco-pochodny (PID). Obecnie zwraca się uwagę na nieliniowy charakter właściwości magnetycznych SRM. Te nieliniowości przełączanych maszyn reluktancyjnych powodują, że tradycyjny regulator PID jest nieodpowiednim wyborem dla urządzeń, w których wymagana jest wysoka dynamika napędu. Algorytm genetyczny (GA) jest manipulowany w celu dostosowania współczynników PID dla napędu SRM. Uzyskane konsekwencje wskazują, że wykorzystanie tych ustalonych algorytmów kontrolera poprawia wydajność w stanie nieustalonym i ustalonym.

4

Aircraft pitch angle control using pole placement approach based on ga and abc optimization techniques

Abdulla Abdulla I., Almaged Mohammed, khather Salam Ibrahim

Przegląd Elektrotechniczny

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2022

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R. 98, nr 3

79--83

EN

The main objective of the present work is designing a pole placement controller for pitch angle control of an aircraft system based on several bio-inspired optimization methods. Initially, a mathematical model of an aircraft pitch system has been derived and formed in state space representation. Then, pole placement approach is designed with the aid of different optimization techniques, including Genetic Algorithms (GA) and Artificial Bee Colony (ABC), to find an optimal value for the feedback gain matrix. The goal is to choose an optimal target values for the closed loop poles of the system by state feedback method and place them at every targeted location anywhere in the left-half of the complex plane ensuring that the closed-loop poles are stable and controllable. This work also compares the performance of GA with that of ABC algorithm based on different time response characteristics. The efficiency of the control systems responses has been analyzed for the sake of deciding which optimization approach will produce better results concerning the controlled pitch angle. Based on the obtained simulation results, it has been noted that ABC based pole placement controller exhibited more efficient results and overweigh the performance of pole placement controllers based on GA

PL

Głównym celem niniejszej pracy jest zaprojektowanie kontrolera rozmieszczenia biegunów do sterowania kątem pochylenia systemu samolotu w oparciu o kilka metod optymalizacji inspirowanych biologią. Początkowo opracowano model matematyczny układu nachylenia samolotu i utworzono go w reprezentacji w przestrzeni stanów. Następnie projektuje się podejście do umieszczania tyczek za pomocą różnych technik optymalizacji, w tym algorytmów genetycznych (GA) i sztucznej kolonii pszczół (ABC), aby znaleźć optymalną wartość macierzy wzmocnienia sprzężenia zwrotnego. Celem jest wybór optymalnych wartości docelowych dla biegunów pętli zamkniętej systemu metodą sprzężenia zwrotnego stanu i umieszczenie ich w każdym docelowym miejscu w dowolnym miejscu w lewej połowie złożonej płaszczyzny, zapewniając stabilność i kontrolę biegunów pętli zamkniętej. Ta praca porównuje również wydajność GA z wydajnością algorytmu ABC w oparciu o różne charakterystyki czasowe odpowiedzi. Skuteczność odpowiedzi układów sterowania została przeanalizowana w celu określenia, które podejście optymalizacyjne przyniesie lepsze wyniki w zakresie kontrolowanego kąta pochylenia. Na podstawie uzyskanych wyników symulacji zauważono, że sterownik układania słupów oparty na ABC wykazał bardziej wydajne wyniki i przewyższał wydajność sterowników układania słupów opartych na GA.

5

An inventory model to study the effect of the probabilistic rate of carbon emission on the profit earned by a supplier

Bhattacharjee Nabajyoti, Sen Nabendu

Operations Research and Decisions

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2021

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Vol. 31, No. 4

5--33

EN

Sensitivity analysis of parameters is usually more important than the optimal solution when it comes to linear programming. Nevertheless, in the analysis of traditional sensitivities for a coefficient, a range of changes is found to maintain the optimal solution. These changes can be functional constraints in the coefficients, such as good values or technical coefficients, of the objective function. When real-world problems are highly inaccurate due to limited data and limited information, the method of grey systems is used to perform the needed optimisation. Several algorithms for solving grey linear programming have been developed to entertain involved inaccuracies in the model parameters; these methods are complex and require much computational time. In this paper, the sensitivity of a series of grey linear programming problems is analysed by using the definitions and operators of grey numbers. Also, uncertainties in parameters are preserved in the solutions obtained from the sensitivity analysis. To evaluate the efficiency and importance of the developed method, an applied numerical example is solved.

6

The consistency of level by level equivalence with V=HOD, the Ground Axiom, and instances of square and diamond

Apter Arthur W.

Bulletin of the Polish Academy of Sciences. Mathematics

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2020

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Vol. 68, no. 1

1--10

EN

We construct via forcing a model for the level by level equivalence between strong compactness and supercompactness in which both V=HOD and the Ground Axiom (GA) are true. In our model, various versions of the combinatorial principles □ and ♢ hold. In the model constructed, there are no restrictions on the class of supercompact cardinals.

7

Global path planning for multiple AUVs using GWO

Panda Madhusmita, Das Bikramaditya, Pati Bibhuti

Archives of Control Sciences

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2020

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Vol. 30, No. 1

77--100

EN

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.

8

Improvement of lumped models in multiple infows rivers by principal component analysis and reliability analysis

Pashazadeh Arash, Javan Mitra

Acta Geophysica

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2019

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Vol. 67, no. 4

1149--1161

EN

Flood routing as an important part of food management is a technique for predicting the fow in downstream of a river channel or reservoir. Lumped, semi-distributed and distributed models have been devised in this regard. The convex and Att-Kin models are capable of simulating foods in single branches, while in reality, rivers and channels are multiple infows. The convex and modifed Att-Kin models as the simplest lumped models in terms of the storage equation were developed based on an equivalent infow for routing the multiple infows rivers in the present study. The genetic algorithm, a quite robust algorithm, was used for parameter estimation of the extended models. The ability of the extended models in simulating the outfow hydrograph of multiple infows systems was tested on two multiple infows case studies. The results of extended models were compared with the equivalent Muskingum infow model. Comparison of the extended models with the Muskingum model showed that the extended models with one parameter less than the Muskingum model could be suitable for use in food routing of multiple infows systems. The efect of infow hydrographs at diferent time steps was investigated by the principal component analysis (PCA) and reliability analysis. The results showed that the outfow hydrograph of the case study was precisely simulated and predicted by the gene expression programming (GEP) and multilayer perceptron (MLP) models. The PCA and reliability analysis results were adopted for the lumped, GEP and MLP models. The outfow hydrograph was precisely simulated and predicted by the GEP and MLP models, while the precision of lumped models (extended convex, extended modifed Att-Kin and Muskingum models) was not increased.

9

Optimizing the process of railway geometrical layout designing with multi-criteria assessment method

Szwaczkiewicz K.

Przegląd Komunikacyjny

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2017

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R. 72, nr 6

17--20

EN

The paper presents the main assumptions of the Multi-criteria assessment method used in process of upgrading the railway geometrical layout. The advantages of metaheuristic search were described. The criteria influencing the investment were defined. The fitness function used in the analysis was described. The example of using the optimization algorithm with help of self developed computer software was described.

PL

W pracy przedstawiono główne założenia opracowanej metody wielokryterialnej oceny stosowanej w procesie modernizacji układów geometrycznych toru. Wyszczególniono główne zalety stosowania metod metaheurystycznego przeszukiwania. Określono kryteria wpływające na inwestycje modernizacyjne. Zdefiniowano zastosowaną w analizie funkcję celu. Przedstawiono przykład zastosowania algorytmu optymalizacyjnego z wykorzystaniem autorskiego programu komputerowego.

10

Vibration optimization of a two-link flexible manipulator with optimal input torques

Pouya M., Vahidi Pashaki P.

Mechanics and Mechanical Engineering

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2017

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Vol. 21, nr 2

253--265

EN

This paper is concerned with the optimal path planning for reduction in residual vibration of two-flexible manipulator. So after presenting the model of a two-link flexible manipulator, the dynamic equations of motion were derived using the assumed modes method. Assuming a desired path for the end effector, the robot was then optimized by considering multiple objective functions. The objective functions should be defined such that in addition to guaranteeing the end effector to travel on the desired path, they can prevent the undesirable extra vibrations of the flexible components. Moreover, in order to assure a complete stop of the robot at the end of the path, the velocity of the end effector at the final point in the path should also reach zero. Securing these two objectives, a time-optimal control may then be applied in order for the robot to travel the path in the minimum duration possible. In all the scenarios, the input motor torques applied to the Two-link are determined as the optimization variables in a given range. The optimization procedures were carried out based on the GA (Genetic Algorithm) and BFGS (Broyden-Fletcher-Goldfarb-Shanno) algorithms, and the results are then compared. It is observe that the BFGS algorithm was able to achieve better results compared to GA running a lower number of iterations. Then the final value of the objective function after optimization indicates the decrease in the vibrations of the end effector at the tip of the flexible link.

11

Applying artificial intelligence algorithms in MOBA games

Wiśniewski M., Niewiadomski A.

Studia Informatica : systems and information technology

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2016

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Vol. 1-2(20)

53--64

EN

Multiplayer Online Battle Arena games focus mainly on struggles between two teams of players. An increasing level of cyberbullying [1] discourages new players from the game and they often chose a different option, that is, a match against opponents controlled by the computer. The behavior of artificial foes can be dynamically fitted to user’s needs, in particular with regard to the difficulty of the game. In this paper we explore different approaches to provide an intelligent behavior of bots basing on more human-like combat predictions rather than instant, scripted behaviors.

12

Designing a Mixed Evaluating System for Green Manufacturing of Automotive Industry

Cao Y., You J., Wang R., Shi Y.

Problemy Ekorozwoju

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2016

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Vol. 11, nr 1

73--86

EN

The article proposed, from a sustainable development perspective, an index system based on Sustainability Balanced Scorecard (SBSC), including the main index of Financial, Internal process, Customer, Learning and growth, Social and the sub- index which comprised 28 indexes to evaluate the Green Manufacturing (GM) of automotive industry. Based on the index system, an evaluation model integrates by back-propagation artificial neural network (BPANN) and genetic algorithm (GA) was introduced. Using established model and indicators evaluated GM in four automotive companies; the key result of the evaluated show that: China’s automotive manufacturing enter-prises still have big room for improvement in respect of customer satisfaction, resource consumption, community service, low-carbon activities etc., so the strategy and management activities that put much pressure on these respect are necessary.

PL

W artykule zaproponowano wykorzystanie systemu wskaźników opartych na Zrównoważonej Karcie Wyników (Sustainability Balanced Scorecard – SBSC). Zgodnie z koncepcją rozwoju zrównoważonego uwzględniono na-stępujące główne wskaźniki: finansowy, procesów wewnętrznych, klienta, wzrostu i uczenia się, społeczny, a także 28 podwskaźników. Celem była ocena Zielonej Produkcji (Green Manufacturing – GM) w przemyśle mo-toryzacyjnym. Wprowadzono model oceny oparty na systemie wskaźników, łączący propagację wsteczną sztucz-nej sieci neuronowej (back-propagation artificial neural network – BPANN) oraz algorytm genetyczny (genetic algorithm – GA). Za pomocą wybranego modelu i wskaźników dokonano oceny Zielonej Produkcji w czterech firmach motoryzacyjnych. Wyniki wskazują, że chińskie przedsiębiorstwa motoryzacyjne mają jeszcze dużo do poprawy w kwestii satysfakcji klienta, zużycia zasobów, pracy społecznej, działań niskoemisyjnych, itp. Ko-nieczne jest zatem obranie strategii oraz gospodarki, które kładą nacisk na wymienione kwestie.

13

Reliability analysis and optimization of equal load-sharing k-out-of-n phased-mission systems

Jiakai C., Yan H., Wei W.

Eksploatacja i Niezawodność

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2015

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Vol. 17, no. 2

250--259

EN

There are many studies on k-out-of-n systems, load-sharing systems (LSS) and phased-mission systems (PMS); however, little attention has been given to load-sharing k-out-of-n systems with phased-mission requirements. This paper considers equal loadsharing k-out-of-n phased-mission systems with identical components. A method is proposed for the phased-mission reliability analysis of the studied systems based on the applicable failure path (AFP). A modified universal generating function (UGF) is used in the AFP-searching algorithm because of its efficiency. The tampered failure rate load-sharing model for the exactly k-out-of-n: F system is introduced and integrated into the method. With the TFR model, the systems with arbitrary load-dependent component failure distributions can be analyzed. According to the time and space complexity analysis, this method is particularly suitable for systems with small k-values. Two applications of the method are introduced in this paper. 1) A genetic algorithm (GA) based on the method is presented to solve the operational scheduling problem of systems with independent submissions. Two theorems are provided to solve the problem under some special conditions. 2) The method is used to select the optimal number of components to make the system reliable and robust.

PL

Istnieje wiele badań na temat systemów typu „k z n”, systemów z podziałem obciążenia (load-sharing systems, LSS) oraz systemów fazowych (tj. systemów o zadaniach okresowych) (phased-missionsystems, PMS); jak dotąd mało uwagi poświęcono jednak systemom typu „k z n” z podziałem obciążenia wymagającym realizacji różnych zadań w różnych przedziałach czasowych. Niniejszy artykuł omawia systemy fazowe typu „k z n” o równym podziale obciążenia przypadającego na identyczne elementy składowe. Zaproponowano metodę analizy niezawodności badanych systemów w poszczególnych fazach ich eksploatacji opartą na pojęciu właściwej ścieżki uszkodzeń (applicablefailurepath, AFP). W algorytmie wyszukującym AFP zastosowano zmodyfikowaną uniwersalną funkcję tworzącą (universal generating function, UGF), która cechuje się dużą wydajnością. Wprowadzono model manipulowanej intensywności uszkodzeń (tamperedfailurerate, TFR) elementów o równym podziale obciążenia dla systemu, w którym liczba uszkodzeń wynosi dokładnie k z n. Model ten włączono do proponowanej metody analizy niezawodności. Przy pomocy modelu TFR można analizować systemy o dowolnych rozkładach uszkodzeń części składowych, gdzie uszkodzenia są zależne od obciążenia. Zgodnie z analizą złożoności czasowej i przestrzennej, metoda ta jest szczególnie przydatna do modelowania układów o małych wartościach k. W pracy przedstawiono dwa zastosowania metody. 1) oparty o omawianą metodę algorytm genetyczny (GA) do rozwiązywania problemu harmonogramowania prac w systemach z niezależnymi podzadaniami. Sformułowano dwa twierdzenia pozwalające na rozwiązanie problemu w pewnych szczególnych warunkach. 2) Wybór optymalnej liczby elementów składowych pozwalającej na zachowanie niezawodności i odporności systemu.

14

Reducing transfer costs of fragments allocation in replicated distributed database using genetic algorithms

Sourati N. K, Ramezni F

Advances in Science and Technology. Research Journal

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2015

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Vol. 9, nr 25

1--6

EN

Distributed databases were developed in order to respond to the needs of distributed computing. Unlike traditional database systems, distributed database systems are a set of nodes that are connected with each other by network and each of nodes has its own database, but they are available by other systems. Thus, each node can have access to all data on entire network. The main objective of allocated algorithms is to attribute fragments to various nodes in order to reduce the shipping cost. Thus, firstly fragments of nodes must be accessible by all nodes in each period, secondly, the transmission cost of fragments to nodes must be reduced and thirdly, the cost of updating all components of nodes must be optimized, that results in increased reliability and availability of network. In this study, more efficient hybrid algorithm can be produced combining genetic algorithms and previous algorithms.

15

Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)

Wazirali R. A., Alzughaibi A. D., Chaczko Z.

International Journal of Electronics and Telecommunications

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2014

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Vol. 60, No. 1

125-128

EN

The report revolve on building construction engineering and management, in which there are a lot of requirements such as well supervision and accuracy and being in position to forecast uncertainties that may arise and mechanisms to solve them. It also focuses on the way the building and construction can minimise the cost of building and wastages of materials. The project will be based of heuristic methods of Artificial Intelligence (AI). There are various evolution methods, but report focus on two experiments Pattern Recognition and Travelling Salesman Problem (TSP). The Pattern Recognition focuses Evolutionary Support Vector Machine Inference System for Construction Management. The construction is very dynamic are has a lot of uncertainties, no exact data this implies that the inference should change according to the environment so that it can fit the reality, therefore there a need of Support Vector Machine Inference System to solve these problems. TSP focus on reducing cost of building construction engineering and also reduces material wastages, through its principals of finding the minimum cost path of the salesman.

16

SMT Versus Genetic and OpenOpt Algorithms : Concrete Planning in the PlanICS Framework

Niewiadomski A., Skaruz J., Penczek W., Szreter M., Jarocki M.

Fundamenta Informaticae

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2014

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Vol. 135, nr 4

451--466

EN

The paper deals with the concrete planning problem (CPP) – a stage of the Web Service Composition (WSC) in the PlanICS framework. The complexity of the problem is discussed. A novel SMT-based approach to CPP is defined and its performance is compared to the standard Genetic Algorithm (GA) and the OpenOpt numerical toolset planner in the framework of the PlanICS system. The discussion of all the approaches is supported by extensive experimental results.

17

WTHD minimisation in hybrid multilevel inverter using biogeographical based optimisation

Kavitha R., Thottungal R.

Archives of Electrical Engineering

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2014

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Vol. 63, nr 2

187--196

EN

Harmonic minimisation in hybrid cascaded multilevel inverter involves complex nonlinear transcendental equation with multiple solutions. Hybrid cascaded multilevel can be implemented using reduced switch count when compared to traditional cascaded multilevel inverter topology. In this paper Biogeographical Based Optimisation (BBO) technique is applied to Hybrid multilevel inverter to determine the optimum switching angles with weighted total harmonic distortion (WTHD) as the objective function. Optimisation based on WTHD combines the advantage of both OMTHD (Optimal Minimisation of Total Harmonic Distortion) and SHE (Selective Harmonic Elimination) PWM. WTHD optimisation has the benefit of eliminating the specific lower order harmonics as in SHEPWM and minimisation of THD as in OMTHD. The simulation and experimental results for a 7 level multilevel inverter were presented. The results indicate that WTHD optimization provides both elimination of lower order harmonics and minimisation of Total Harmonic Distortion when compared to conventional OMTHD and SHE PWM. Experimental prototype of a seven level hybrid cascaded multilevel inverter is implemented to verify the simulation results.

18

Nonlinear Blind Source Separation of Multi-Sensor signals for Marine Diesel Engine Fault Diagnosis

Chenxing S., Zhe T., Yuelei Z.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 1b

271--274

EN

Marine diesel engines are the heart of the ships. They provide the power for the normal propulsion of the vessels. Any unexpected failures occurred in the marine diesel engines may lead to terrible accident. It is therefore imperative to monitor the marine diesel engines to prevent impending faults. In the present work, a new defect detection method for the marine diesel engines using the artificial intelligence has been proposed. The vibration signals of the marine diesel engine were recorded by the multi-channel sensors. The nonlinear independent component analysis (NICA) was adopted as the data fusion approach to find the characteristic vibration signals of the marine diesel engine fault from the multiply sensor collections. Then the Empirical Mode Decomposition (EMD) was employed to extract the feature vector of the fused vibration signals. Lastly, the Genetic Algorithm-Chaos and RBF neural network was used to recognize the fault patterns of the marine diesel engine. The experimental tests were implemented in a real ship to evaluate the effectiveness of the proposed diagnosis approach. The diagnosis results have showed that distinguished fault features have been extracted and the fault identification accuracy is satisfactory. In addition, the classification rate of the proposed method is superior to the traditional linear ICA based methods.

PL

Wykorzystano nieliniową niezależną analizę składników NICA do diagnostyki wibracji silnika Diesla. Zastosowano metodę empirycznej dekompozycji EMD do separacji sygnałów. Następnie wykorzystano sieci neuronowe i algorytm genetyczny do identyfikacji uszkodzeń.

19

Application of artificial neural network and genetic algorithm to healthcarewaste prediction

Arabgol S., Ko H. S.

Journal of Artificial Intelligence and Soft Computing Research

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2013

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Vol. 3, No. 4

243--250

EN

Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

20

Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms

Ma Z. S.

Fundamenta Informaticae

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2013

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Vol. 122, nr 3

173--206

EN

Natural populations are dynamic in both time and space. In biological populations such as insects, spatial distribution patterns are often studied as the first step to characterize population dynamics. In nature, the spatial distribution patterns of insect populations are considered as the emergent expression (property) of individual behaviors at population levels and are fine-tuned or optimized by natural selection. This inspiration prompts us to investigate the possibly similar mechanisms in Genetic Algorithms (GA) populations. In this study, we introduce the mathematical models for the spatial distribution patterns of insect populations to GA with the conjecture that the emulation of biological populations in nature may lead to computational improvement. In particular, we introduce three modeling approaches from the research of spatial distribution patterns of insect populations: (i) probability distribution modeling approach, (ii) aggregation index approach, and (iii) Taylor’s (1961, 1977) Power Law, Iwao’s (1968, 1976) Mean Crowding Model and Ma’s (1991c) population aggregation critical density (PACD), to characterize populations in GA. With these three approaches, we investigate four mappings from the research field of insect spatial distribution patterns to GA populations in order to search for possible counterpart mechanisms or features in GA. They are: (i) mapping insect spatial distribution patterns to GA populations or allowing GA populations to be controlled by stochastic distribution models that describe insect spatial distributions; (ii) mapping insect population distribution to GA population fitness distribution via Power Law and PACD modeling; (iii)mapping population aggregation dynamics to GA fitness progression across generations (or fitness aggregation dynamics in GA) via insect population aggregation index; (iv) mapping insect population sampling model to optimal GA population sizing. With regard to the mapping (i), the experiment results show the significant improvements in GA computational efficiency in terms of the reduced fitness evaluations and associated costs. This prompts us to suggest using probability distribution models, or what we call stochastic GA populations, to replace the fixed-size population settings. We also found the counterpart for the second mapping, the wide applicability of Power Law and Mean Crowding model to the fitness distribution in GA populations. The testing of the third and fourth mappings is very preliminary; we use example cases to suggest two further research problems: the potential to use fitness aggregation dynamics for controlling the number of generations iterated in GA searches, and the possibility to use fitness aggregation distribution parameters [(obtained in mapping (ii)] in determining the optimum population size in GA. A third interesting research problem is to investigate the relationship between mapping (i) and (iii), i.e., the controlling of both population sizes and population generations.