Wyniki wyszukiwania - BazTech

1

(µ + λ) evolution strategy with socio-cognitive mutation

Urbanczyk Aleksandra, Kucaba Krzysztof, Wojtulewicz Mateusz, Kisiel-Dorohinicki Marek, Rutkowski Leszek, Duda Piotr, Kacprzyk Janusz, Yao Xin, Chong Siang Yew, Byrski Aleksander

Journal of Automation Mobile Robotics and Intelligent Systems

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2024

|

Vol. 18, No. 1

1--11

EN

Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.

2

New ideas in Lagrangian relaxation for a scheduling problem with the weighted tardiness criterion

Rudy Jarosław, Idzikowski Radosław, Smutnicki Czesław, Banaszak Zbigniew, Bocewicz Grzegorz

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 2

235--251

EN

We consider an extension of Lagrangian relaxation methods for solving the total weighted tardiness scheduling problem on a single machine. First, we investigate a straightforward relaxation method and decompose it into upper and lower subproblems. For the upper subproblem we propose an alternative solving method in the form of a local search metaheuristic. We also introduce a scaling technique by arbitrary numbers to reduce the complexity of the problem and confront it with greatest common divisor scaling. Next, we propose a novel alternative relaxation approach based on aggregating constraints. We discuss the properties and implementation of this new approach and a technique to further reduce its computational complexity. We perform a number of computer experiments on instances based on the OR-Library generation scheme to illustrate and ascertain the numerical properties of the proposed methods. The results indicate that for larger instances the proposed alternative relaxation and scaling approaches have a much better convergence rate with little to no decrease in solution quality. The results also show that the proposed local-search metaheuristic is a viable alternative to the existing solving methods.

3

Optimization of Job Shop Scheduling Problem by Genetic Algorithms: Case Study

Sahar Habbadi, Herrou Brahim, Sekkat Souhail

Management and Production Engineering Review

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2023

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Vol. 14, No. 3

44--56

EN

The Job Shop scheduling problem is widely used in industry and has been the subject of study by several researchers with the aim of optimizing work sequences. This case study provides an overview of genetic algorithms, which have great potential for solving this type of combinatorial problem. The method will be applied manually during this study to understand the procedure and process of executing programs based on genetic algorithms. This problem requires strong decision analysis throughout the process due to the numerous choices and allocations of jobs to machines at specific times, in a specific order, and over a given duration. This operation is carried out at the operational level, and research must find an intelligent method to identify the best and most optimal combination. This article presents genetic algorithms in detail to explain their usage and to understand the compilation method of an intelligent program based on genetic algorithms. By the end of the article, the genetic algorithm method will have proven its performance in the search for the optimal solution to achieve the most optimal job sequence scenario.

4

Application of the path-repairing technique and virus optimization algorithm for the dimensional synthesis of four-bar mechanisms

Grabski Jakub Krzysztof, Sopa Martyna, Mrozek Agata

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e134, 2023

EN

This paper considers the synthesis of the four-bar mechanism. It is treated here as an optimization problem, in which an objective function is defined. To solve this problem, a metaheuristic called the virus optimization algorithm is employed. Furthermore, a new path-repairing technique recently published by Sleesongsom and Bureerat is applied instead of the very common technique related to the application of a penalty function. This makes the search by means of the metaheuristic more efficient. Furthermore, the obtained results are very accurate.

5

Studium przypadku skuteczności nowych metod optymalizacji roju w porównaniu do metod znanych

Baumgart Jan, Sangho Belco

Studia i Materiały Informatyki Stosowanej

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2021

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nr 1

47--50

PL

Porównianie skuteczności nowych metod optymalizacji roju w porównaniu z metodami znanymi w dziedzinie. Inspirowane naturą algorytmy metaheurystyczne stają się coraz bardziej popularne w rozwiązywaniu problemów optymalizacyjnych. Dzięki ich popularności niemal codziennie możemy zobaczyć nowepodejścia i proponowane rozwiązania. W tym artykule przedstawię porównanie, które pokaże kilka najnowszychprac z tej dziedziny w porównaniu z niektórymi algorytmami traktowanymi jako podstawa dziedziny. Głównymcelem było porównanie ostatnio wprowadzonych algorytmów roju i określenie, kiedy nowe rozwiązania są faktycznie szybsze i bardziej precyzyjne. Podsumowując, czy przetestowane nowe podejścia są lepsze niż obecne,dobrze znane i ugruntowane w terenie algorytmy. Algorytmy brane pod uwagę w tej pracy to: Particle SwarmOptimization [5], Artifical Bee Colony [3], Elephant Herding Optimization [7], Whale Optimization [4] i Gras-shopper Optimization [6].Algorytmy uznawane za nowe w tej dziedzinie porównano z dwoma popularnymi idobrze znanymi algorytmami metaheurystycznymi pod względem dokładności znalezionych rozwiązań i szybkości. Zgodnie z wynikami eksperymentów większość porównywanych nowych algorytmów dawała zadowalającewyniki w użytkowaniu.

EN

Comparing the effectiveness of new methods of swarm optimization in comparison with knownmethods. Nature-inspired metaheuristic algorithms are becoming more and more popular in solving optimization problems. Thanks to their popularity, we can see new approaches and proposed solutions almost everyday. In this article, I will present a comparison that will show some of the most recent works in this fieldcompared to some algorithms considered as the basis of the field. The main goal was to compare the recently introduced swarm algorithms and determine when new solutions are actually faster and more precise. Inconclusion, are the new approaches tested better than the current, well-known and field-grounded algorithms?The algorithms considered in this paper are Particle Swarm Optimization, Artifical Bee Colony, Elephant Herding Optimization, Whale Optimization, and Grasshopper Optimization. Algorithms considered new inthis field were compared with two popular and well-known metaheuristic algorithms in terms of accuracy ofsolutions found and speed. According to the experimental results, most of the compared new algorithms gave satisfactory results in use.

6

Multi-objective evolutionary algorithms and some of their applications in reliability

Coello Coello Carlos A., Castillo Tapia Ma Guadalupe

Safety and Reliability of Systems and Processes

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2021

|

Summer Safety and Reliability Seminar 2021

45--57

EN

Multi-objective optimization has become increasingly important, mainly because many real-world problems are multi-objective in nature. The complexity of many of such problems has made necessary the use of metaheuristics. From them, the use of multi-objective evolutionary algorithms has become very popular mainly because of their ease of use and flexibility. In this chapter, we provide a short review of multi-objective evolutionary algorithms and some of their applications in reliability. In the final part of the chapter, some possible paths for future research in this area are also discussed.

7

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.

8

Skuteczność nowoczesnych algorytmów optymalizacji czerpiących inspirację z procesów naturalnych

Klawikowska Zuzanna, Puchalski Bartosz

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

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2020

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Nr 71

35--40

PL

Ze względu na nieistnienie uniwersalnego algorytmu optymalizacji rozwiązującego wszystkie problemy naukowotechniczne opracowywanie nowych i wydajniejszych obliczeniowo algorytmów optymalizacyjnych wciąż jest popularnym zadaniem. Przeglądając literaturę z dziedziny optymalizacji można zauważyć trend tworzenia „wymyślnych” algorytmów opartych na procesach naturalnych. W artykule sprawdzono skuteczność nowopowstałych algorytmów meta-heurystycznych zainspirowanych życiem owadów i zwierząt – czarnych wdów (algorytm BWO) oraz szarego wilka (algorytm GWO). Skuteczność działania wybranych algorytmów porównano z klasycznym algorytmem quasi-Newtonowskim BFGS oraz strategią ewolucyjną CMA-ES, które charakteryzują się solidnym uwarunkowaniem matematycznym. W celach porównawczych wykorzystano 3 wybrane funkcje testowe. W ramach badań sprawdzono również wpływ liczby zmiennych decyzyjnych na czas uzyskiwania rozwiązania.

EN

Due to the lack of a universal optimization algorithm which solves all scientific and technical problems, developing new and more computationally efficient optimization algorithms is still a popular challenge. Reviewing the literature on optimization there is a trend to create "fancy" algorithms based on natural processes. The article examines the effectiveness of newly developed meta-heuristic algorithms inspired by insects and animals - black widows (BWO algorithm) and grey wolf (GWO algorithm). The effectiveness of the selected algorithms was compared with the classical quasi-Newtonian BFGS algorithm and the evolutionary strategy CMA-ES, which are characterized by a solid mathematical background. Three selected benchmark functions were used for comparison purposes. The study also included a test of the influence of the number of design variables on the time complexity.

9

Fine tuning of agent-based evolutionary computing

Mizera Michal, Nowotarski Pawel, Byrski Aleksander, Kisiel-Dorohinicki Marek

Journal of Artificial Intelligence and Soft Computing Research

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2019

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Vol. 9, No. 2

81--97

EN

Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

10

Contemporary Methods for Graph Coloring as an Example of Discrete Optimization

Bilski Adrian

International Journal of Electronics and Telecommunications

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2019

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Vol. 65, No. 2

235--243

EN

This paper provides an insight into graph coloring application of the contemporary heuristic methods. It discusses a variety of algorithmic solutions for The Graph Coloring Problem (GCP) and makes recommendations for implementation. The GCP is the NP-hard problem, which aims at finding the minimum number of colors for vertices in such a way, that none of two adjacent vertices are marked with the same color.With the advent of multicore processing technology, the metaheuristic approach to solving GCP reemerged as means of discrete optimization. To explain the phenomenon of these methods, the author makes a thorough survey of AI-based algorithms for GCP, while pointing out the main differences between all these techniques.

11

Efficient Approaches for Solving a Multiobjective Energy-aware Job Shop Scheduling Problem

González Miguel A., Oddi Angelo, Rasconi Riccardo

Fundamenta Informaticae

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2019

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Vol. 167, nr 1-2

93--132

EN

One of the most recent and interesting trends in intelligent scheduling is trying to reduce the energy consumption in order to obtain lower production costs and smaller carbon footprint. In this work we consider the energy-aware job shop scheduling problem, where we have to minimize at the same time an efficiency-based objective, as is the total weighted tardiness, and also the overall energy consumption. We experimentally show that we can reduce the energy consumption of a given schedule by delaying some operations, and to this end we design a heuristic procedure to improve a given schedule. As the problem is computationally complex, we design three approaches to solve it: a Pareto-based multiobjective evolutionary algorithm, which is hybridized with a multiobjective local search method and a linear programming step, a decomposition-based multiobjective evolutionary algorithm hybridized with a single-objective local search method, and finally a constraint programming approach. We perform an extensive experimental study to analyze our algorithms and to compare them with the state of the art.

12

Deep Learning Optimization Tasks and Metaheuristic Methods

Biedrzycki Rafał, Zawistowski Paweł, Twardowski Bartłomiej

Fundamenta Informaticae

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2019

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Vol. 168, nr 2-4

185--218

EN

In this paper we identify and formulate two optimization tasks solved in connection with training DL models and constructing adversarial examples. This guides our review of optimization methods commonly used within the DL community. Simultaneously, we present findings from the literature concerning metaheuristics and black-box optimization. We focus on well-known optimizers suitable for solving ℝN tasks, which achieve good results on benchmarks and in competitions. Finally, we look into the research connected with utilizing metaheuristic optimization methods in combination with deep learning models.

13

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.

14

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.

15

Benchmark Tests on Heuristic Methods in the Darts Game

Książek K., Połap D., Woźniak M.

International Journal of Electronics and Telecommunications

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2018

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Vol. 64, No. 2

115--121

EN

Games are among problems that can be reduced to optimization, for which one of the most universal and productive solving method is a heuristic approach. In this article we present results of benchmark tests on using 5 heuristic methods to solve a physical model of the darts game. Discussion of the scores and conclusions from the research have shown that application of heuristic methods can simulate artificial intelligence as a regular player with very good results.

16

Parallel patterns determination in solving cyclic flow shop problem with setups

Bożejko W., Chaczko Z., Uchroński M., Wodecki M.

Archives of Control Sciences

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2017

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Vol. 27, no. 2

183--195

EN

The subject of this work is the new idea of blocks for the cyclic flow shop problem with setup times, using multiple patterns with different sizes determined for each machine constituting optimal schedule of cities for the traveling salesman problem (TSP). We propose to take advantage of the Intel Xeon Phi parallel computing environment during so-called ’blocks’ determination basing on patterns, in effect significantly improving the quality of obtained results.

17

Heuristics in optimal detailed design of precast road bridges

Yepes V., Martí J. V., García-Segura T., González-Vidosa F.

Archives of Civil and Mechanical Engineering

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2017

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Vol. 17, no. 4

738--749

EN

This paper deals with the cost optimization of road bridges consisting of concrete slabs prepared in situ and two precast-prestressed U-shaped beams of self-compacting concrete. It shows the efficiency of four heuristic algorithms applied to a problem of 59 discrete variables. The four algorithms are the Descent Local Search (DLS), a threshold accepting algorithm with mutation operation (TAMO), the Genetic Algorithm (GA), and the Memetic Algorithm (MA). The heuristic optimization algorithms are applied to a bridge with a span length of 35 m and a width of 12 m. A performance analysis is run for the different heuristics, based on a study of Pareto optimal solutions between execution time and efficiency. The best results were obtained with TAMO for a minimum cost of 104 184€. Among the key findings of the study, the practical use of these heuristics in real cases stands out. Furthermore, the knowledge gained from the investigation of the algorithms allows a range of values for the design optimization of such structures and pre-dimensioning of the variables to be recommended.

18

FLC control for tuning exploration phase in bio-inspired metaheuristic

Kiełkowicz K., Grela D.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2016

|

Vol. 16, no. 2

32--38

EN

Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.

19

Challenges for nurse rostering problem and opportunities in hospital logistics

Simić D., Simić S., Milutinović D., Djordjević J.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

195--202

EN

In the last 45 years nurse scheduling has received considerable attention in the research community. Nurse rostering can be described as a task of finding a duty roster for a set of nurses in such a way that the rosters comply with work regulations and meet the management’s requests. The objective varies from minimizing the costs of float nurses or minimizing under-staffing to maximizing the degree to which the nurses’ requests are met. In logistics, one aspect is optimization of the steady flow of materials through a network of transport links and storage nodes, and the other is, coordination of a sequence of resources, such as staffing and scheduling clinical resources. The period up to 2000 is characterized by using mathematical programming and objective functions to solve nurse rostering problem. In the period after 2000 the focus of researches aimed at solving nurse rostering and scheduling problem becomes implementation of meta-heuristics and multi-objective functions. The aim of this paper is to present the latest researches conducted in last ten years.

20

Comparing Problem Solving Strategies for NP-hard Optimization Problems

Hidalgo-Herrero M., Rabanal P., Rodríguez I., Rubio F.

Fundamenta Informaticae

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2013

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Vol. 124, nr 1/2

1--25

EN

NP-complete problems are particularly hard to solve. Unless P=NP, any algorithm solving an NP-complete problem takes exponential time in the worst case. The intrinsic difficulty of NP-complete problems when we try to optimally solve them with computers seems to apply to humans too. Intuitively, solving NP-complete problems requires taking a series of choices where each choice we take disables many subsequent choices, but the scope of dependencies between these mutually exclusive choices cannot be bound. Thus, the problem cannot be split into smaller subproblems in such a way that their solutions can be computed independently and easily combined for constructing the global solution (as it happens in divide and conquer algorithms). Moreover, for each choice, the space of subsequent subproblems to be considered for all possible choice elections does not collapse into a polynomial size set (as it happens in dynamic programming algorithms). Thus, intuitively, in NP-complete problems any choice may unboundedly affect any other, and this difficulty seems to puzzle humans as much as computers. In this paper we conduct an experiment to systematically analyze the performance of humans when solving NP-complete problems. For each problem, in order to measure partial fulfillment of the decision problem goal, we consider its NP-hard optimization version. We analyze the human capability to compute good suboptimal solutions to these problems, we try to identify the kind of problem instances which make humans compute the best and worst solutions (including the dependance of their performance on the size of problem instances), and we compare their performance with computational heuristics typically used to approximately solve these problems. We also interview experiment participants in order to infer the most typical strategies used by them in experiments, as well as how these strategies depend on the form and size of problem instances.