Simple Probabilistic Population-Based Optimization

Lin, Ying-Chi; Claub, Martin; Middendorf, Martin

doi:10.1109/tevc.2015.2451701

Cited by 11 publications

(4 citation statements)

References 41 publications

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“…For simplicity, we directly cite the results of these two winners from the corresponding competitions (CEC'2013 4 and CEC'2015). 5 Tables SXXIV-SXXVIII in the supplementary material present the comparison results with respect to PR and SR between LAMS-ACO and these two winners with each table associated with one accuracy level. The best PR results are highlighted in bold in these tables and the last row (b/e/w) 6 of these tables counts the number of functions on which LAMS-ACO is better than, equivalent to or worse than the compared winner, respectively.…”

Section: E Comparisons With Winners Of Cec Competitionsmentioning

confidence: 99%

“…From Tables SXXIV-SXXVIII in the supplementary material, we can get the following findings. 4 https://github.com/mikeagn/CEC2013/tree/master/NichingCompetition 2013FinalData 5 https://github.com/mikeagn/CEC2013/tree/master/NichingCompetition 2015FinalData 6 In this experiment, owing to the absence of the detailed results of these two winners in each run in the corresponding competitions, whether LAMS-ACO is better than, equivalent to or worse than the compared winners is judged just by the averaged PR results without any statistical test analysis. Thus, to tell apart from the results in the last section, the number of the functions on which LAMS-ACO is better than, equivalent to or worse than the compared winner, is respectively, denoted by "b/e/w" instead of "w/t/l".…”

Section: E Comparisons With Winners Of Cec Competitionsmentioning

confidence: 99%

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Adaptive Multimodal Continuous Ant Colony Optimization

Yang

Chen

et al. 2017

IEEE Trans. Evol. Computat.

271

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Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

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Section: E Comparisons With Winners Of Cec Competitionsmentioning

confidence: 99%

Section: E Comparisons With Winners Of Cec Competitionsmentioning

confidence: 99%

Adaptive Multimodal Continuous Ant Colony Optimization

Yang

Chen

et al. 2017

IEEE Trans. Evol. Computat.

271

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“…In this study several evolutionary computation methods have been benchmarked and it was concluded that PACO is the method of choice. Several variants of PACO for the TSP have been investigated recently in [11].…”

Section: Background and Related Liter-aturementioning

confidence: 99%

A Population Based ACO Algorithm for the Combined Tours TSP Problem

Clauss

Lotzmann

Middendorf

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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In this paper we apply a Population based Ant Colony Optimization (PACO) algorithm for solving the following new version of the Traveling Salesperson problem that is called the Combined Tours TSP (CT-TSP). Given are a set of cities, for each pair of cities a cost function and an integer k. The aim is to find a set of k (cyclic) tours, i.e., each city is contained exactly once in each tour and each tour returns to its origin city, which have minimum total costs. In this paper the case of finding two tours is studied where the costs of one tour depends on the other tour. Each pair of cities has a distance and a weight which influence the costs of the tours. The weight is used to define if it is advantageous or disadvantageous when the corresponding pair of cities is contained, i.e., neighbouring, in both tours. Different heuristics that the ants of the PACO use for the construction of the tours are compared experimentally. One result is that it is (often) advantageous when the heuristic for the second tour is different from the heuristic for the first tour such that the former heuristic uses knowledge about the first tour.

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“…Population-based evolutionary optimizations, initially inspired by biological process, are considered as a major way of solving complex optimization problems. In the past few decades, a number of population-based evolutionary algorithms are proposed such as simulated annealing (SA), differential evolution (DE), Artificial Immune System(AIS) [2,3], particle swarm optimization (PSO), bee colony optimization (BCO), genetic algorithms (GAs), populationbased increamental learning (PBIL) and ant colony optimization (ACO) [4]. Among them, SA, DE, PSO and BCO are roughly proposed to find solutions in continuous domains mainly for function optimization while GA, PBIL and ACO are mainly used for solving combinatorial optimization problems [5].…”

Section: Introductionmentioning

confidence: 99%

Dynamic island model based on spectral clustering in genetic algorithm

Meng¹,

Wu²,

Ellis³

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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How to maintain relative high diversity is important to avoid premature convergence in population-based optimization methods. Island model is widely considered as a major approach to achieve this because of its flexibility and high efficiency.The model maintains a group of sub-populations on different islands and allows sub-populations to interact with each other via predefined migration policies. However, current island model has some drawbacks. One is that after a certain number of generations, different islands may retain quite similar, converged subpopulations thereby losing diversity and decreasing efficiency. Another drawback is that determining the number of islands to maintain is also very challenging. Meanwhile initializing many sub-populations increases the randomness of island model. To address these issues, we proposed a dynamic island model (DIM-SP) which can force each island to maintain different subpopulations, control the number of islands dynamically and starts with one sub-population. The proposed island model outperforms the other three state-of-the-art island models in three baseline optimization problems including job shop scheduler problem, travelling salesmen problem and quadratic multiple knapsack problem.

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Simple Probabilistic Population-Based Optimization

Cited by 11 publications

References 41 publications

Adaptive Multimodal Continuous Ant Colony Optimization

Adaptive Multimodal Continuous Ant Colony Optimization

A Population Based ACO Algorithm for the Combined Tours TSP Problem

Dynamic island model based on spectral clustering in genetic algorithm

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