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 g...
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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.
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 approache...
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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.
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,...
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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.
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...
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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.
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) evol...
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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.
There are many different varieties of population-based algorithms. They are interesting techniques for investigating of the search space of solutions and can be used, among others, to solve optimization problems. They...
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ISBN:
(纸本)9783319912530;9783319912523
There are many different varieties of population-based algorithms. They are interesting techniques for investigating of the search space of solutions and can be used, among others, to solve optimization problems. They usually start from initialization of a population of individuals, each of which encodes parameters of a single solution to the problem under consideration. After initialization, the preselected individuals are processed in a way that depends on the specifics of the algorithm. Therefore, properly implemented population initialization can significantly improve the algorithm's operation and increase the quality of obtained results. This article describes a new population initialization algorithm. Its characteristic feature is the marginalization of those areas of the search space, in which once localized individuals were assessed as not satisfying. The proposed algorithm is of particular importance for problems in which no information is available that can improve the search procedure (black-box optimization). To test the proposed algorithm simulations were carried out using well-known benchmark functions.
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 desi...
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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.
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 alg...
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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).
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...
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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.
Structural bias is a recently identified property of optimisation algorithms, causing them to favour certain regions of the search space over others, independently of the objective function. Since structural bias can ...
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Structural bias is a recently identified property of optimisation algorithms, causing them to favour certain regions of the search space over others, independently of the objective function. Since structural bias can adversely affect the progress of optimisation, a better understanding of it is needed in order to inform the theory and practice of algorithm design. For example, it is generally accepted that larger populations are favoured when solution quality is paramount and time constraints are permissive. However, common variants of both Genetic algorithms and Particle Swarm Optimisation have been found to exhibit structural bias that increases with population size. Herein we investigate structural bias in popular variants of Differential Evolution (DE), and attempt to identify which algorithm features trigger its emergence. In particular, we focus on the (often overlooked) constraint handling mechanism. Our results suggest that DE is generally robust to structural bias. Only one of the variants studied - DE/current-to-best/1/bin - shows clear signs of bias, however this is mitigated by a judicious choice of constraint handling technique. These findings contribute towards explaining the widespread success of DE in algorithm comparison studies;its robustness to structural bias represents the absence of a factor that may confound other algorithms. (C) 2019 Published by Elsevier Inc.
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