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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

Optimization of daily operations in the marine industry using ant colony optimization (ACO)-An artificial intelligence (AI) approach

Sardar A., Anantharaman M., Garaniya V., Khan F.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2023

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Vol. 17 No. 2

290--295

EN

The maritime industry plays a crucial role in the global economy, with roughly 90% of world trade being conducted through the use of merchant ships and more than a million seafarers. Despite recent efforts to improve reliability and ship structure, the heavy dependence on human performance has led to a high number of casualties in the industry. Decision errors are the primary cause of maritime accidents, with factors such as lack of situational awareness and attention deficit contributing to these errors. To address this issue, the study proposes an Ant Colony Optimization (ACO) based algorithm to design and validate a verified set of instructions for performing each daily operational task in a standardised manner. This AI-based approach can optimise the path for complex tasks, provide clear and sequential instructions, improve efficiency, and reduce the likelihood of human error by minimising personal preference and false assumptions. The proposed solution can be transformed into a globally accessible, standardised instructions manual, which can significantly contribute to minimising human error during daily operational tasks on ships.

3

A nature inspired collision avoidance algorithm for ships

Lazarowska A.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2023

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Vol. 17 No. 2

341--346

EN

Nature inspired algorithms are regarded as a powerful tool for solving real life problems. They do not guarantee to find the globally optimal solution, but can find a suboptimal, robust solution with an acceptable computational cost. The paper introduces an approach to the development of collision avoidance algorithms for ships based on the firefly algorithm, classified to the swarm intelligence methods. Such algorithms are inspired by the swarming behaviour of animals, such as e.g. birds, fish, ants, bees, fireflies. The description of the developed algorithm is followed by the presentation of simulation results, which show, that it might be regarded as an efficient method of solving the collision avoidance problem. Such algorithm is intended for use in the Decision Support System or in the Collision Avoidance Module of the Autonomous Navigation System for Maritime Autonomous Surface Ships.

4

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

5

The application of adaboost.m1 based on ant colony optimization to classify the risk of delay in the pharmaceutical supply chain

Wyrembek Mateusz

LogForum

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2023

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Vol. 19, no. 2

236--275

EN

Background: The purpose of this article is to present the developed AdaBoost.M1 based on Ant Colony Optimization (hereby referred to as ACOBoost.M1 throughout the study) to classify the risk of delay in the pharmaceutical supply chain. This study investigates one research hypothesis, namely, that the ACOBoost.M1 can be used to predict the risk of delay in the supply chain and is characterized by a high prediction performance. Methods: We developed a machine learning algorithm based on Ant Colony Optimization (ACO). The meta-heuristic algorithm ACO is used to find the best hyperparameters for AdaBoost.M1 to classify the risk of delay in the pharmaceutical supply chain. The study used a dataset from 4PL logistics service provider. Results: The results indicate that ACOBoost.M1 may predict the risk of delay in the supply chain and is characterized by a high prediction performance. Conclusions: The present findings highlight the significance of applying machine learning algorithms, such as the AdaBoost.M1 model with Ant Colony Optimization for hyperparameter tuning, to manage the risk of delays in the pharmaceutical supply chain. These findings not only showcase the potential for machine learning in enhancing supply chain efficiency and robustness but also set the stage for future research. Further exploration could include investigating other optimization techniques, machine learning models, and their applications across various industries and sectors.

6

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Singsathid Pirapong, Puphasuk Pikul, Wetweerapong Jeerayut

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 2

243--266

EN

Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the prob- abilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.

7

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.

8

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.

9

Ant colony based coverage optimization in wireless sensor networks

Khoshrangbaf Mina, Akram Vahid Khalilpour, Challenger Moharram

Annals of Computer Science and Information Systems

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2022

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Vol. 32

155--159

EN

Maximizing the covered area of wireless sensor networks while keeping the connectivity between the nodes is one of the challenging tasks in wireless sensor networks deployments. In this paper we propose an ant colony-based method for the problem of sensor nodes deployment to maximize the coverage area. We model sensor locations as a graph and use an adapted ant colony optimization-based method to find the best places for each sensor node. To keep the connectivity of the sensor network, every sensor must be covered by the other sensors; this is a hard constraint that is applied to the cost function as a penalty. The proposed algorithm is evaluated with different number of sensor nodes and sensing ranges. The simulation results showed that increasing the number of iterations in the algorithm generates better coverage ratio with the same number of nodes.

10

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.

11

An ant algorithm for the maximum number of 3-cliques in 3-partite graphs

Schiff Krzysztof

Control and Cybernetics

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2021

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Vol. 50, No. 2

347--358

EN

The problem of finding the maximum number of d- vertices cliques (d = 3) in d-partite graph (d = 3) when graph density q is lower than 1 is an important problem in combinatorial optimization and it is one of many NP-complete problems. For this problem a meta-heuristic algorithm has been developed, namely an ant colony optimization algorithm. In this paper a new development of this ant algorithm and experimental results are presented. The problem of finding the maximum number of 3-vertices cliques can be encountered in computer image analysis, computer vision applications, automation and robotic vision systems. The optimal solution of this problem boils down to finding a set of 3-vertices cliques in a 3-partite graph and this set should have cardinality as high as possible. The elaborated ant colony algorithm can be easily modified for d-dimensional problems, that is for finding the maximum number of d-vertices cliques in a d-partite graph.

12

Population diversity in ant-inspired optimization algorithms

Byrski Aleksander, Węgrzyński Krzysztof, Radwański Wojciech, Starzec Grażyna, Starzec Mateusz, Bargiel Monika, Urbańczyk Aleksandra, Kisiel-Dorohinicki Marek

Computer Science

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2021

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T. 22 (3)

297–320

EN

Measuring the diversity in evolutionary algorithms that work in real-value search spaces is often computationally complex, but it is feasible; however, measuring the diversity in combinatorial domains is practically impossible. Nevertheless, in this paper we propose several practical and feasible diversitymeasurement techniques that are dedicated to ant colony optimization algorithms, leveraging the fact that we can focus on a pheromone table even though an analysis of the search space is at least an NP problem where the direct outcomes of the search are expressed and can be analyzed. Besides sketching out the algorithms, we apply them to several benchmark problems and discuss their efficacy.

13

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.

14

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.

15

Metaheuristic approach to optimal power flow using mixed integer distributed ant colony optimization

Suresh Vishnu, Janik Przemyslaw, Jasinski Michal

Archives of Electrical Engineering

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2020

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Vol. 69, nr 2

335--348

EN

This paper presents the application of an improved ant colony optimization algorithm called mixed integer distributed ant colony optimization to optimize the power flow solution in power grids. The results provided indicate an improvement in the reduction of operational costs in comparison with other optimization algorithms used in optimal power flow studies. The application was realized to optimize power flow in the IEEE 30 and the IEEE 57 bus test cases with the objective of operational cost minimization. The optimal power flow problem described is a non-linear, non-convex, complex and heavily constrained problem.

16

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.

17

Load Balancing Based on Optimization Algorithms: An Overview

Mbarek Fatma, Mosorov Volodymyr

Journal of Telecommunications and Information Technology

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2019

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nr 4

3--12

EN

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other signiﬁcant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to eﬃciently handle load balancing issues.

18

ACO-Inspired Energy-Aware Routing Algorithm for Wireless Sensor Networks

Yamamoto Ryo, Nishibu Seira, Yamazaki Taku, Okamura Yasushi, Tanaka Yoshiaki

Journal of Telecommunications and Information Technology

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2019

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nr 1

5--13

EN

Multi-hop networks, such as WSNs, become an object of increasing attention as an emerging technology which plays an important role for practical IoT applications. These multi-hop networks generally consist of mobile and small terminals with limited resources, which makes them vulnerable to various network status changes. Moreover, the limited nature of terminal resources available, especially in terms of battery capacity, is one of the most important issues to be addressed in order to prolong their operating time. In order to ensure eﬃcient communications in such networks, much research has already been conducted, especially in the ﬁeld of routing and transmission technologies. However, conventional approaches adopted in the routing ﬁeld still suﬀer from the so-called energy hole problem, usually caused by unbalanced communication loads existing due to diﬃculties in adaptive route management. To address this issue, the present paper proposes a novel routing algorithm that utilizes ACO-inspired routing based on residual energy of terminals. Operational evaluation reveals its potential to ensure balanced energy consumption and to boost network performance.

19

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.

20

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

|

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.