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Content available Global path planning for multiple AUVs using GWO
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.
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.
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.
4
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
EN
This paper presents an FPGA-based (field-programmable gate array) hybrid metaheuristic GA (genetic algorithm)-PSO (particle swarm optimization) algorithm for mobile robots to find an optimal path between a starting and ending point in a grid environment. GA has been combined with PSO in evolving new solutions by applying crossover and mutation operators on solutions constructed by particles. This hybrid algorithm avoids the premature convergence and time complexity in conventional GA and PSO algorithms. The initial feasible path generated from the hybrid GAPSO planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Experimental results are conducted to show the merit of the proposed hybrid GA-PSO path planner for global path planning for mobile robots.
PL
W artykule zaprezentowano algorytm dla mobilnych robotów poszukujący optymalnej ścieżki między punktem startu i końcowym. Algorytm wykorzystuje układy FPGA i bazuje na algorytmach genetycznych i mrówkowych.
EN
In this paper, an efficient mapping of intellectual property (IP) cores onto a scalable multiprocessor system-on-chip with a k-ary 2-mesh network-on-chip is performed. The approach is to place more affine IP cores closer to each other reducing the number of traversed routers. Affinity describes the pairwise relationship between the IP cores quantified by an amount of exchanged communication or administration data. A genetic algorithm (GA) and a mixed-integer linear programming (MILP) solution use the affinity values in order to optimize the IP core mappings. The GA generates results faster and with a satisfactory quality relative to MILP. Realistic benchmark results demonstrate that a tradeoff between administration and communication affinity significantly improves application performance.
EN
The purpose of this work was to compare two forms of genetic algorithm (complete and incomplete graph version) which solves Orienteering Problem (OP). While in most papers concerning OP graph is complete and satisfies triangle inequality, in our versions such assumptions may not be satisfied. It could be more practical as transport networks are graphs which do not have to satisfy those conditions. In such cases, graphs are usually complemented with fictional edges before they can be used by classic OP solving algorithms which operate on complete graphs. This paper answers the question: Is it better (in terms of results quality and time consumption) to transform graphs to classic OP form before running algorithm (complete graph version) or to solve OP on graphs without any assumptions and changes (incomplete graph version)? The computer experiment was conducted on the real transport network in Poland and its results suggest that it is worth checking both versions of the algorithm on concrete networks.
PL
Celem pracy było porównanie dwóch odmian algorytmu (wersja dla grafu pełnego i niepełnego) rozwiązujących Orienteering Problem (OP). W większości artykułów dotyczących OP graf jest pełny, a jego krawędzie spełniają nierówność trójkąta, natomiast w naszej wersji takie założenia mogą nie być spełnione. Może to być bardziej praktyczne ponieważ sieci transportowe są grafami, ktore nie muszą spełniać tych warunków. W takich przypadkach grafy są zazwyczaj uzupełniane fikcyjnymi krawędziami, a następnie działają na nich algorytmy rozwiązujące klasyczną wersje OP, które operują na grafie pełnym. Artykuł odpowiada na pytanie: czy pod względem jakości wyników i czasu obliczeń lepiej jest przekształcać graf do klasycznej formy OP przed uruchomieniem algorytmu w wersji dla grafu pełnego czy rozwiązywać OP na grafie niezmienionym i nie spełniającym dodatkowych założeń (wersja dla grafu niepełnego)? Eksperyment został przeprowadzony na prawdziwej sieci transportowej w Polsce, a jego wyniki sugerują, że warto sprawdzać obie wersje algorytmu na konkretnych sieciach.
EN
In this paper we developed an efficient optimal robust watermarking technique using genetic algorithm (GA) for images of Indian historical monuments and their corresponding names. The watermarks are embedded into the HL and LH frequency coefficients in the Haar wavelet transform domain. Since the embedding technique is blind, it does not require the original image in the watermark extraction. We also develop an optimization technique using the GA to search for the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. We analyze the performance of the proposed watermarking technique in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC). The experimental and the comparative results show that the proposed technique can achieve a good robustness against most of the attacks which are included in this study. For typical image quality, the proposed technique outperforms the existing one with a PSNR of 36 dB and the NC value of 0.96.
19
Content available remote Material parameters identification by use of hybrid GA
EN
Purpose: of this paper is to develop material parameters identification algorithm for yield criterion BBC2003 using global optimization techniques. Design/methodology/approach: An algorithm proposed is based on use of error minimization function, which allows considering over-constraining. Due to strong nonlinearity of the problem considered a number of solutions is available. In order to determine global extreme two stage GA (global optimization technique) is treated. Findings: Numerical material parameters identification algorithm is developed. An approach provided allows reducing significantly the dimension of the nonlinear system before its numerical solution. Convergence to global extreme can be expected due to global optimization technique employed. Research limitations/implications: An analysis is done by keeping formability analysis in mind and only material parameters involved in yield criterion in space of principal stresses are considered. Thus the results can be generalized by including terms corresponding to shear stresses. Practical implications: Advanced yield criteria like BBC2003 are still not used extensively due to the complexities accrued: increasing number of material parameters (additional tests), a complex non-linear programming problem. An algorithm proposed simplifies the material parameters identification process for considered yield criteria BBC2003. The formability analysis of the 6000 series aluminium alloy sheet AA6181-T4 is considered as a case study and used for testing the algorithm proposed. Originality/value: In the case of posed optimization problem the dimension of the design space is reduced from six to two. Over-constraining and under-constraining are considered in algorithm (situations, where number of unknown parameters is not equal with the number of given constraints, are covered).
20
Content available remote Intelligent modelling in manufacturing
EN
Purpose: Modeling of production systems is very important and makes optimization of complicated relation in production system possible. The purpose of this paper is introducing artificial techniques, like Genetic Algorithms in modelling and optimization of job shop scheduling in production environment and in programming of CNC machine tools. Design/methodology/approach: Conventional methods are not suitable for solving such complicated problems. Therefore Artificial Intelligent method was used. We apply Genetic Algorithm method. Genetic Algorithms are computation methods owing their power in particular to autonomous mechanisms in biological evolution, such as selection, "survival of the fittest" (competition), and recombination. Findings: In example solutions are developed for an optimization problem of job shop scheduling by natural selection. Thus no explicit knowledge was required about how to create a good solution: the evolutionary algorithm itself implicitly builds up knowledge about good solutions, and autonomously absorbs knowledge. CNC machining time was significant shorter by using GA method for NC programming. Research limitations/implications: The system was developed for PC and tested in simulation process. It needs to be tested more in detail in the real manufacturing environment. Practical implications: It is suitable for small and medium-sized companies. Human errors are avoid or at lower level. It is important for engineers in job - shops. Originality/value: The present paper is a contribution to more intelligent systems in production environment. It used genetic based methods to solve engineering problem.
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