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16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lapa, Krystian Cpalka, Krzysztof Wang, Lipo Czestochowa Tech Univ Inst Computat Intelligence Czestochowa Poland Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9783319590639;9783319590622

In this paper a new method based on a population-based algorithm with flexible selectable operators for nonlinear modeling is proposed. This method enables usage of any types of exploration and exploitation operators, typical for population-based algorithms. Moreover, in proposed approach each solution from population encodes activity and parameters of these operators. Due to this, they can be selected dynamically in the evolution process. Such approach eliminates the need for determining detailed mechanism of the population-based algorithm. For the simulations typical nonlinear modeling benchmarks were used.

关键词： population-based algorithms Selection of evolutionary operators Neuro-fuzzy systems Nonlinear modeling Exploration and exploitation

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Local Optima in Diversity Optimization: Non-trivial Offspring population is Essential 18th

Local Optima in Diversity Optimization: Non-trivial Offsprin...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Antipov, Denis Neumann, Aneta Neumann, Frank Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA Australia

ISBN: (纸本)9783031700705;9783031700712

The main goal of diversity optimization is to find a diverse set of solutions which satisfy some lower bound on their fitness. Evolutionary algorithms (EAs) are often used for such tasks, since they are naturally designed to optimize populations of solutions. This approach to diversity optimization, called EDO, has been previously studied from theoretical perspective, but most studies considered only EAs with a trivial offspring population such as the (mu + 1) EA. In this paper we give an example instance of a k-vertex cover problem, which highlights a critical difference of the diversity optimization from the regular single-objective optimization, namely that there might be a locally optimal population from which we can escape only by replacing at least two individuals at once, which the (mu + 1) algorithms cannot do. We also show that the (mu + lambda) EA with lambda >= mu can effectively find a diverse population on k-vertex cover, if using a mutation operator inspired by Branson and Sutton (TCS 2023). To avoid the problem of subset selection which arises in the (mu+lambda) EA when it optimizes diversity, we also propose the (1(mu) + 1(mu)) EAD, which is an analogue of the (1 + 1) EA for populations, and which is also efficient at optimizing diversity on the k-vertex cover problem.

关键词： Diversity Optimization population-based algorithms Theory Landscape Analysis Vertex Cover

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population Management Approaches in the OPn Algorithm 20th

Population Management Approaches in the OPn Algorithm

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20th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lapa, Krystian Cpalka, Krzysztof Slowik, Adam Czestochowa Tech Univ PL-42201 Czestochowa Poland Koszalin Univ Technol PL-75453 Koszalin Poland

ISBN: (纸本)9783030879860;9783030879853

This paper deals with the problem of selecting the population size for the population-based algorithm with dynamic selection of operators (OPn). This research was undertaken to check how population size changes affect the optimization of problems in which both the parameters of the solution and its structure should be selected. Moreover, variants in which the size of the population changes dynamically were considered. The simulations were performed for a small selection/variety of examples of control problems in which the structures and parameters of controllers based on PID systems had to be selected.

关键词： population-based algorithms Evolutionary algorithms Operators selection population size

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A population-based Framework for Solving the Job Shop Scheduling Problem 13th

A Population-Based Framework for Solving the Job Shop Schedu...

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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Jedrzejowicz, Piotr Ratajczak-Ropel, Ewa Wierzbowska, Izabela Gdynia Maritime Univ Gdynia Poland

ISBN: (纸本)9783030880811;9783030880804

The paper proposes the framework named MPF, extending the Mushroom Picking Metaheuristics and originally proposed earlier by the authors. The framework can be used for solving combinatorial optimization problems. In the current study, the framework has been used for solving instances of the Job Shop Scheduling Problem (JSSP). The framework allows defining several solutions improving agents. Agents work in parallel trying to improve solutions. Solutions are maintained on two levels - common memory and sub-populations for each thread. The framework provides functionality allowing the implementation of a strategy for maintenance of threads and the common memory, including the information exchange between them. For the JSSP implementation, we propose 5 types of autonomous agents. The computational experiment carried out using benchmark datasets has confirmed the good performance of the proposed approach in terms of solutions quality and computation times.

关键词： Metaheuristics population-based algorithms Job Shop Scheduling Parallel computation

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A Novel Image Segmentation based on Clustering and population-based Optimisation 11th

A Novel Image Segmentation Based on Clustering and Populatio...

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11th International Conference on Swarm Intelligence (ICSI)

作者： Mousavirad, Seyed Jalaleddin Schaefer, Gerald Ebrahimpour-Komleh, Hossein Korovin, Iakov Sabzevar Univ New Technol Fac Engn Sabzevar Iran Loughborough Univ Dept Comp Sci Loughborough Leics England Univ Kashan Dept Comp Engn Kashan Iran Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030539559;9783030539566

Image segmentation is an essential step in image processing and computer vision with many image segmentation algorithms having been proposed in the literature. Among these, clustering is one of the prominent approaches to achieve segmentation. Traditional clustering algorithms have been used extensively for this purpose, although they have disadvantages such as dependence on initialisation conditions and a tendency to find only local optima. To overcome these disadvantages, population-based metaheuristic algorithms can be applied. In this paper, we propose a novel clustering algorithm based on human mental search (HMS) for image segmentation. HMS is a relatively new population-based metaheuristic inspired from the manner of searching in online auctions. HMS comprises three operators: mental search, which explores the neighbourhood of candidate solutions using Levy flight;grouping, which clusters candidate solutions;and moving candidate solutions towards a promising area. To verify the efficacy of the proposed algorithm, we conduct several experiments based on different criteria including mean cost function value, statistical analysis and image segmentation criteria. The obtained results confirm superior performance of our proposed algorithm compared to competitors.

关键词： Image segmentation Clustering Optimisation population-based algorithms Human mental search

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Already Moderate population Sizes Provably Yield Strong Robustness to Noise

Already Moderate Population Sizes Provably Yield Strong Robu...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Antipov, Denis Doerr, Benjamin Ivanova, Alexandra Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA Australia Ecole Polytech Inst Polytech Paris Lab Informat LIX CNRS Palaiseau France HSE Univ Skoltech Moscow Russia

ISBN: (纸本)9798400704949

Experience shows that typical evolutionary algorithms can cope well with stochastic disturbances such as noisy function evaluations. In this first mathematical runtime analysis of the (1 + lambda) and (1, lambda) evolutionary algorithms in the presence of prior bit-wise noise, we show that both algorithms can tolerate constant noise probabilities without increasing the asymptotic runtime on the OneMax benchmark. For this, a population size.. suffices that is at least logarithmic in the problem size lambda. The only previous result in this direction regarded the less realistic one-bit noise model, required a population size super-linear in the problem size, and proved a runtime guarantee roughly cubic in the noiseless runtime for the OneMax benchmark. Our significantly stronger results are based on the novel proof argument that the noiseless offspring can be seen as a biased uniform crossover between the parent and the noisy offspring. We are optimistic that the technical lemmas resulting from this insight will find applications also in future mathematical runtime analyses of evolutionary algorithms.

关键词： Noisy optimization Runtime analysis Theory population-based algorithms

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Heuristics to Avoid Redundant Solutions on population-based Multimodal Continuous Optimization

Heuristics to Avoid Redundant Solutions on Population-Based ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Pasti, Rodrigo Von Zuben, Fernando J. Maia, Renato Dourado de Castro, Leandro Nunes Univ Estadual Campinas UNICAMP Lab Bioinformat & Bioinspired Comp LBiC Sch Elect & Comp Engn FEEC Campinas SP Brazil Univ Estadual Montes Claros Dept Comp Sci Montes Claros Brazil Mackenzie Univ Sao Paulo Fac Elect Engn & Comp Sci Nat Comp Lab Sao Paulo Brazil

ISBN: (纸本)9781424478354

In population-based meta-heuristics, the generation and maintenance of diversity seem to be crucial to deal with multimodal continuous optimization. However, usually this crucial aspect is not an inherent feature of generally adopted meta-heuristics. In this paper, we propose to associate diversity maintenance with the detection and elimination of redundant candidate solutions in the search space, more specifically candidate solutions located at the same attraction basin of a local optimum. Two low computational cost heuristics are proposed to detect redundancy, in a pairwise comparison of candidate solutions and by extracting local features of the fitness landscape at runtime. Those heuristics are not tied to a specific class of algorithms, and are thus able to be incorporated into a broad range of population-based meta-heuristics, and even into multiple executions of non-population-based algorithms. In a set of experimental results, the two heuristics were implemented as an attached module of an already existing multipopulation meta-heuristics, and the results indicate that they operate properly, no matter the number and conformation of the attraction basins in multimodal optimization problems.

关键词： Multimodal functions population-based algorithms redundant solutions heuristics meta-heuristics

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Parameter Optimization algorithms for Evolving Rule Models Applied to Freshwater Ecosystems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第6期18卷 793-806页

作者： Cao, Hongqing Recknagel, Friedrich Orr, Philip T. Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia Seqwater City East Qld 4002 Australia

Predictive rule models for early warning of cyanobacterial blooms in freshwater ecosystems were developed using a hybrid evolutionary algorithm (HEA). The HEA has been designed to evolve IF-THEN-ELSE model structures using genetic programming and to optimize the stochastical constants contained in the model using population-based algorithms. This paper intensively investigates the performances of the following six alternative population-based algorithms for parameter optimization (PO) of rule models within this hybrid methodology: 1) hill climbing (HC);2) simulated annealing (SA);3) genetic algorithm (GA);4) differential evolution (DE);5) covariance matrix adaptation evolution strategy (CMA-ES);and 6) estimation of distribution algorithm (EDA). The comparative study was carried out by predictive modeling of chlorophyll-a concentrations and the potentially toxic cyanobacterium Cylindrospermopsis raciborskii cell concentrations based on water quality time-series data in Lake Wivenhoe, Queensland, Australia, from 1998 to 2009. The experimental results demonstrate that with these PO methods, the rule models discovered by the HEA proved to be both predictive and explanatory whose IF condition indicates threshold values for some crucial water quality parameters. When comparing different PO algorithms, HC always performed best followed by DE, GA, and EDA, while CMA-ES performed worst and the performance of SA varied with different data sets.

关键词： Cyanobacterial blooms evolutionary algorithm genetic programming population-based algorithms

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Stepladder determinative brain storm optimization

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APPLIED INTELLIGENCE 2022年第14期52卷 16799-16817页

作者： Sovatzidi, Georgia Iakovidis, Dimitris K. Univ Thessaly Dept Comp Sci & Biomed Informat Papasiopoulou Str 2-4 Lamia 35131 Greece

Brain Storm Optimization (BSO) is a swarm intelligence algorithm that mimics the brainstorming process of human beings. Since the introduction of BSO, many attempts have been made to improve the performance of the algorithm, mainly with respect to its convergence time and quality of solutions. In this work, we introduce a novel modified version of BSO named Stepladder Determinative BSO (S-DBSO). This algorithm is inspired by a brainstorming technique for decision making, proposed in the field of psychology. This is called Stepladder Technique, and it considers a creative brainstorming process that guarantees the equal participation of all members, even the most introverted of the group, to express their thoughts. The main achievements of this study include: 1) computational modeling of the Stepladder brainstorming process for algorithmic optimization;2) optimization of the search ability of the algorithm towards the best solutions, after having given the right directionality to the newly generated individuals;3) better convergence speed with fewer iterations of algorithm required, compared to other state-of-the-art methods and 4) avoidance of being trapped into local optima. Experiments based on well-recognized benchmarks, including the test suite of the competition for single-objective real parameter optimization of the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC 2017), validate that S-DBSO outperforms the original and state-of-the-art variations of BSO, as well as other state-of-the-art metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Bat Algorithm (BA).

关键词： Swarm intelligence population-based algorithms Brain storm optimization (BSO) Stepladder technique

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Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第10期13卷 1483-1491页

作者： Dong, Xueshi Lin, Qing Xu, Min Cai, Yongle Qingdao Univ Coll Comp Sci & Technol Qingdao Shandong Peoples R China Wuhan Univ Comp Sch Wuhan Hubei Peoples R China Chang Jiang Waterway Inst Planning & Design Wuhan Hubei Peoples R China

Coloured travelling salesman problem (CTSP) is an extended model of multiple travelling salesman problems (MTSPs), as one kind of problem in combination optimisation problems, which has been applied to many real-world planning problems such as multi-machine engineering system (MES). population-based algorithms such as genetic algorithm (GA) and simulated annealing (SA) algorithm are only used to solve small- or medium-scale cases in which the city number is <2000. Furthermore, in term of the convergence speed and solution quality, their performances have further improvement space. Since many real-world problems can be modelled by large-scale CTSP, it is necessary to study better algorithms to solve large-scale CTSP. Since artificial bee colony algorithm (ABC) can show good performance in solving combination optimisation problems, and therefore this study applied improved ABC algorithms to solve large-scale CTSP. The modified ABC algorithms use generating neighbourhood solution (GNS) to improve ABC for this problem, two limitation and optimisation conditions are applied into GNS during the process of reinserting cities. The extensive experiments verify that the improved algorithms can demonstrate better solution quality than the compared algorithms for large-scale CTSP.

关键词： simulated annealing search problems travelling salesman problems genetic algorithms artificial bee colony algorithm large-scale CTSP artificial bee colony algorithm combination optimisation problems multiple travelling salesman problems real-world planning problems multimachine engineering system population-based algorithms ABC algorithms large scale coloured travelling salesman problem genetic algorithm simulated annealing algorithm generating neighbourhood solution

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