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1

Fixed fleet open vehicle routing problem: Mathematical model and a modified ant colony optimization

Yousefikhoshbakht Majid, Didehvar Farzad, Rahmati Farhad, Ahmed Zakir Hussain

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 1

art. no. e148253

EN

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances.

2

ACO control of three-level series active power filter based fuel cells

Masmoudi Mohamed, Tedjini Hamza, Nasri Abdelfatah

Przegląd Elektrotechniczny

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2023

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R. 99, nr 6

1--6

EN

Hydrogen has been generally accepted as a power source with productivity with zero emissions ideal for the development of mobile power and stationary electricity. This paper presented an integration of PEMFC into a series filter for preventing the propagation of harmonics and minimizing the current ripple and preserving the AC micro-grid. For better performance, the ants' colony optimization algorithm is used on the software side and three-level NPC in the hardware parts of this filter.

PL

Wodór jest powszechnie akceptowany jako źródło energii o wydajności z zerową emisją, idealne do rozwoju mobilnej i stacjonarnej energii elektrycznej. W artykule przedstawiono integrację PEMFC z filtrem szeregowym w celu zapobiegania propagacji harmonicznych i minimalizacji tętnienia prądu oraz zachowania mikrosieci prądu przemiennego. Aby uzyskać lepszą wydajność, algorytm optymalizacji kolonii mrówek jest używany po stronie oprogramowania, a trzypoziomowy NPC w części sprzętowej tego filtra

3

Fuzzy-based firefly and ACO algorithm for densely deployed WSN

Sharma Tripti, Mohapatra Amar Kumar, Tomar Geetam

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 2

223--246

EN

Most of the wireless sensor networks (WSNs) used in healthcare and security sectors are affected by the battery constraints, which cause a low network lifetime problem and prevents these networks from achieving their maximum performance. It is anticipated that by combining fuzzy logic (FL) approximation reasoning approach with WSN, the complex behavior of WSN will be easier to handle. In healthcare, WSNs are used to track activities of daily living (ADL) and collect data for longitudinal studies. It is easy to understand how such WSNs could be used to violate people’s privacy. The main aim of this research is to address the issues associated with battery constraints for WSN and resolve these issues. Such an algorithm could be successfully applied to environmental monitoring for healthcare systems where a dense sensor network is required and the stability period should be high.

4

A bioinspired optimization strategy: to minimize the travel segment of the nozzle to accelerate the fused deposition modeling process

Sridhar Sundarraj, Aditya K., Venkatraman Ramamoorthi, Venkatesan M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2023

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Vol. 71, nr 4

art. no. e146236

EN

The fused deposition modeling process of digital printing uses a layer-by-layer approach to form a three-dimensional structure. Digital printing takes more time to fabricate a 3D model, and the speed varies depending on the type of 3D printer, material, geometric complexity, and process parameters. A shorter path for the extruder can speed up the printing process. However, the time taken for the extruder during printing (deposition) cannot be reduced, but the time taken for the extruder travel (idle move) can be reduced. In this study, the idle travel of the nozzle is optimized using a bioinspired technique called "ant colony optimization" (ACO) by reducing the travel transitions. The ACO algorithm determines the shortest path of the nozzle to reduce travel and generates the tool paths as G-codes. The proposed method’s G-code is implemented and compared with the G-code generated by the commercial slicer, Cura, in terms of build time. Experiments corroborate this finding: the G-code generated by the ACO algorithm accelerates the FDM process by reducing the travel movements of the nozzle, hence reducing the part build time (printing time) and increasing the strength of the printed object.

5

Implementation of a DSP- TMS320F28335 based state feedback with optimal design of PI controller for a speed of BLDC motor by ant colony optimization

Wongkhead Sompod, Tunyasrirut Satean

Przegląd Elektrotechniczny

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2021

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R. 97, nr 7

7--12

EN

This paper presents the design for control system and Implementation of a DSP TMS320F28335 Based State Feedback with Optimal Design of PI Controller for control Speed of BLDC Motor by genetic algorithm (GA), particle swarm optimization (PSO) and Ant Colony Optimization (ACO) for comparison the control Speed of BLDC Motor System. The experimental results show that Optimal Design of PI controller is the ACO controller, was able to control speed of BLDC motor. In load and non-load condition, control system can maintain the level of speed in steady state. According to the responses of the reference signal, this can be concluded that controlling speed round using an ACO controller is highly effective in controlling the speed of BLDC motor.

PL

W artykule przedstawiono projekt systemu sterowania i implementację DSP TMS320F28335 opartego na sprzężeniu zwrotnym z optymalnym kontrolerem PI do sterowania prędkością silnika BLDC za pomocą algorytmu genetycznego (GA), optymalizacji roju cząstek (PSO) i optymalizacji kolonii mrówek (ACO) dla Porównanie kontroli prędkości systemu silnika BLDC. Wyniki eksperymentalne pokazują, że optymalną konstrukcją kontrolera PI jest kontroler ACO, który był w stanie kontrolować prędkość silnika BLDC. W warunkach obciążenia i bez obciążenia układ sterowania może utrzymać poziom prędkości w stanie ustalonym. Zgodnie z odpowiedziami sygnału odniesienia można stwierdzić, że sterowanie prędkością obrotową za pomocą kontrolera ACO jest wysoce skuteczne w sterowaniu prędkością silnika BLDC.

6

An automated classification of HEp-2 cellular shapes using Bag-of-keypoint features and Ant Colony Optimization

Devanathan Kanchana, Ganesan Kavitha, Swaminathan Ramakrishnan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

376--390

EN

In this study, an attempt has been made to differentiate HEp-2 cellular shapes using Bag-of keypoint features and optimization. For this, the images are considered from a publicly available database. To increase the cell structure visibility, the images are pre-processed using edge-sensitive local contrast enhancement. Further, the Speeded-up Robust Feature (SURF) keypoints are extracted and Bag-of-keypoints for each shape are generated. These features are subjected to Ant Colony Optimization (ACO) algorithm for feature selection. The optimal features obtained are then fed to Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) classifiers. Results show that the ACO algorithm can identify the optimal features that characterize the cellular shapes. SVM and kNN are able to differentiate between the shapes with an average classification accuracy of 93.6% and 94.8% respectively. Since differential diagnosis of HEp-2 cellular shapes is significant in the disease-specific prognosis and treatment, this study seems to be clinically relevant.

7

InterCriteria Analyzis of Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem

Fidanova Stefka, Ganzha Maria, Roeva Olympia

Annals of Computer Science and Information Systems

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2021

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Vol. 25

173--180

EN

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. In this paper we apply InterCriteria analysis (ICrA) on hybrid Ant Colony Optimization (ACO) algorithm for Multiple Knapsack Problem (MKP). The aim is to study the algorithm behavior comparing with traditional ACO algorithm. Based on the obtained numerical results and on the ICrA approach the efficiency and effectiveness of the proposed local search procedure are confirmed.

8

Performance Comparison of Routing Protocols in Opportunistic Networks

Prabha Chander, Kumar Rakesh

Annals of Computer Science and Information Systems

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2020

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Vol. 24

87--90

EN

In today's world doing data transfer in delay tolerant networks (DTN) environment is a challenging task. In DTN nodes are characterized to meet opportunistically to do routing and data transfer. In opportunistic environment no end-to-end path exists between destination and source. The contacts are made opportunistic while coming in contact for a short span of time. All communication is within this span only. Due to this feature the DTN's are sometimes recognized as Opportunistic Networks (ON's). The rules are not predefined here for choosing the next node as applicable in conventional schemes of routing. In this paper the performance of opportunistic routing protocols have been investigated namely PRoPHET, Spray and Wait, SimBet, Bubble Rap in terms of robustness and scalability. The concept of Ant Colony Optimization is used to find optimal routes while doing routing decision. The performance of SimBet and Bubble Rap is better with respect to throughput as they belong to social context aware category of protocols. Performance is evaluated in terms of packet dropped and overhead ratio also. The overhead ratio is better in SimBet and Bubble Rap as compared to Spray and Wait and PRoPHET. Depending on buffer size, speed, contact times these routing strategies shows variable performance. The result indicates that the social aware algorithms have the ability and capacity to exchange/carry information faster and improve the connectivity in ON's.

9

Reconstruction of selected operating parametersof a thermoelectric device

Nowak Iwona, Buchalik Ryszard, Nowak Grzegorz

Computer Assisted Methods in Engineering and Science

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2019

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Vol. 26, no. 1

36--46

EN

This paper presents preliminary research aimed at recognizing some selected operating parameters of a thermoelectric device. The inverse problem was formulated, for the solution of which a population heuristics (Ant Colony Optimization) was used. In the inverse task, selected parameters important for the cell operation were reconstructed based on relatively easy to obtain temperature measurements within heat exchangers and appropriate measurements of electrical quantities. The heuristics used, reconstructs the estimated variables, minimizing the differences between data from the measurements and data calculated in the model for their determined values. Since inverse tasks, as ill-conditioned problems, are characterized by high sensitivity to measurement errors, the tests began with calculations based on numerically generated data in order to fully maintain control of their disturbances.

10

Ant colony optimization algorithm for workforce planning: influence of the evaporation parameter

Fidanova Stefka, Luque Gabriel, Roeva Olympia, Ganzha Maria

Annals of Computer Science and Information Systems

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2019

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Vol. 18

177--181

EN

Optimization of the production process is important for every factory or organization. The better organization can be done by optimization of the workforce planing. The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done. The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence. The problem is to select employers and to assign them to the jobs to be performed. The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions. We apply Ant Colony Optimization Algorithm to solve the problem. We investigate the algorithm performance according evaporation parameter. The aim is to find the best parameter setting.

11

An Analytical Study for the Role of Fuzzy Logic in Improving Metaheuristic Optimization Algorithms

Vij Sonakshi, Jain Amita, Tayal Devendra, Castillo Oscar

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

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Vol. 12, No. 4

11--27

EN

The research applications of fuzzy logic have always been multidisciplinary in nature due to its ability in handling vagueness and imprecision. This paper presents an analytical study in the role of fuzzy logic in the area of metaheuristics using Web of Science (WoS) as the data source. In this case, 178 research papers are extracted from it in the time span of 1989-2016. This paper analyzes various aspects of a research publication in a scientometric manner. The top cited research papers, country wise contribution, topmost organizations, top research areas, top source titles, control terms and WoS categories are analyzed. Also, the top 3 fuzzy evolutionary algorithms are extracted and their top research papers are mentioned along with their topmost research domain. Since neuro fuzzy logic poses feasible options for solving numerous research problems, hence a section is also included by the authors to present an analytical study regarding research in it. Overall, this study helps in evaluating the recent research patterns in the field of fuzzy metaheuristics along with envisioning the future trends for the same. While on one hand this helps in providing a new path to the researchers who are beginners in this field as they can start exploring it through the analysis mentioned here, on the other hand it provides an insight to professional researchers too who can dig a little deeper in this field using knowledge from this study.

12

Heuristics applying stochastic information as tools for thermoacoustic standing-wave engine optimization

Nowak I.

Computer Assisted Methods in Engineering and Science

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2018

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Vol. 25, no. 1

3--19

EN

In this article, two numerical methods for solving engineering problems defined as multicriteria optimiza-tion and inverse problem are presented. In particular, thisstudy deals with the optimization of the designof thermoacoustic engine in the frame in which both types of tasks are solved. The first proposed heuristicserves to find many p-optimal solutions simultaneously, which represents a compromise between usuallymutually contradictory goals at work. Based on them, the full Pareto front is approximated. The inverseproblem solution reproduces parameters for solutions located on a designated front but those that arenot found in multicriteria optimization. In this article, the RACO heuristics are proposed for determiningp-optimal solutions and the Bayesian approach is introduced as a method for solving ill-conditioned inverseproblems. Optimization of the construction of the thermoacoustic engine is aimed at verifying proposedmethodology and present the possibility of using both methods in engineering problems. The problemdiscussed in this article is formulated and the numerical methods used in the solution are presented indetails.

13

A comparison of ant colony optimization and genetic algorithm for solving the traveling salesman problem

Ochelska-Mierzejewska J.

Journal of Applied Computer Science

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2016

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Vol. 24, nr 1

51--66

EN

Every company in today’s world faces the constant challenge of cost reduction. For distribution and transport service providers, cost cutting appears to be the main operational goal. The successful companies seek to develop an optimal routes for their fleets to minimize the costs and guarantee a timely delivery of the goods. With a growing informatization of the industrial world, it is worth considering the use of intelligent systems as a possible way of solving various types of decision problems, which in turn can contribute to the reduction of costs incurred by a company. Such systems enable multidimensional data analysis and to provide information useful in decision making. The paper investigates the use of the genetic algorithm and the ants colony optimization algorithm as a solution to the travelling salesman problem. It has been shown that both methods provide satisfactory results in solving the problem under examination.

14

Real ant colony optimization as a tool for multi-criteria problems

Nowak I., Nowak G.

Computer Assisted Methods in Engineering and Science

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2015

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Vol. 22, no. 3

255--265

EN

This paper presents a population-based heuristic method – a real ant colony optimization (RACO) as a tool for multi-criteria optimization problems. The idea of multi-criteria optimization is discussed and the necessary modifications of RACO are proposed. These modifications made possible to use the method to simultaneously search many Pareto-optimal solutions. The method was numerically tested in problems of benchmark-type and used for solving simple engineering problems. This article presents and discusses all results obtained in tests, and two different approaches to multi-criteria optimization are additionally compared (search then decision and decision then search).

15

An ant colony system for team orienteering problems with time windows

Montemanni R., Gambardella L.M.

Foundations of Computing and Decision Sciences

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2009

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Vol. 34, No. 4

287-306

EN

This paper discusses a heuristic approach for Team Orienteering Problems with Time Windows. The method we propose takes advantage of a solution model based on a hierarchic generalization of the original problem, which is combined with an Ant Colony System algorithm. Computational results on benchmark instances previously adopted in the literature suggest that the algorithm we propose is effective in practice.