Multimodal Estimation of Distribution Algorithms

Yang, Qiang; Chen, Wei-Neng; Li, Yun; Chen, C. L. Philip; Xu, Xiangmin; Zhang, Mengjie

doi:10.1109/tcyb.2016.2523000

Cited by 161 publications

(56 citation statements)

References 57 publications

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“…The Big Bang-Big Crunch (BB-BC) algorithm is improved for multimodal optimization because of its low computational cost [21]. The Estimation of Distribution Algorithms (EDAs) are improved to deal with multimodal one by considering their advantages in preserving high diversity [22].…”

Section: > Replace This Line With Your Paper Identification Number mentioning

confidence: 99%

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang

Ding

Zhou

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This work proposes an optimization strategy for multi-robot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this work presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A pre-judgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed guiding mutation operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.Index Terms-Multi-robot task allocation, multimodal optimization, niching immune-based optimization algorithm, Softmax regression, guiding mutation operator.

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Section: > Replace This Line With Your Paper Identification Number mentioning

confidence: 99%

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang

Ding

Zhou

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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“…Obviously niching can be introduced to other meta-heuristics as well, such as Artificial Immune Systems (AIS) [60], [61], Ant Colony Optimization (ACO) [62]- [64], and Cultural Algorithms (CA) [65]. It is also possible to induce niching behaviour through probalistic modeling building, e.g., via an Estimated Distributed Algorithm (EDA) [66]. Please refer to [17] for further information on these niching methods.…”

Section: Other Meta-heuristicsmentioning

confidence: 99%

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Epitropakis

Deb

et al. 2017

IEEE Trans. Evol. Computat.

230

101

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Abstract-Multi-modal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation is an important topic which has practical relevance in problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO prolems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the more recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts on leveraging the capabilities of niching to facilitate various optimization (e.g., multi-objective and dynamic optimization) and machine learning (e.g., clustering, feature selection, and learning ensembles) tasks. A list of successful applications of niching methods to real-world problems is provided to demonstrate that niching methods are able to provide solutions that are difficult for conventional optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.

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“…To further evaluate the efficiency of C-EDA 2 , we compared it with LMCEDA [11], LMSEDA [11] and RS-CMSA [18]. LMCEDA and LMSEDA are two well-established multimodal EDAs, which have been briefly introduced in Section 1.…”

Section: Performance Of C-edamentioning

confidence: 99%

“…Yang et al [13] proposed a novel maintaining and processing multiple submodels technique to enhance the performance of EDA on multimodal problems. Yang et al [11] developed tow multimodal EDAs (MEDAs) based on crowding and speciation, respectively. They further enhanced the two MEDAs with dynamic clustersizing strategy and local search scheme for relieving the parameter sensitivity and improving the solution accuracy, the resultant algorithms were named as LMCEDA and LMSEDA.…”

Section: Introductionmentioning

confidence: 99%

Niching an archive-based gaussian estimation of distribution algorithm via adaptive clustering

Liang

Ren

Pang

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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As  a model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization. However, traditional Gaussian EDA (GEDA) may suffer from premature convergence and has a high risk of falling into local optimum when dealing with multimodal problem. In this paper, we first attempts to improve the performance of GEDA by utilizing historical solutions and develops a novel archive-based EDA variant. The use of historical solutions not only enhances the search efficiency of EDA to a large extent, but also significantly reduces the population size so that a faster convergence could be achieved. Then, the archive-based EDA is further integrated with a novel adaptive clustering strategy for solving multimodal optimization problems. Taking the advantage of the clustering strategy in locating different promising areas and the powerful exploitation ability of the archive-based EDA, the resultant algorithm is endowed with strong capability in finding multiple optima. To verify the efficiency of the proposed algorithm, we tested it on a set of well-known niching benchmark problems and compared it with several state-of-the-art niching algorithms. The experimental results indicate that the proposed algorithm is competitive.

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Multimodal Estimation of Distribution Algorithms

Cited by 161 publications

References 57 publications

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Niching an archive-based gaussian estimation of distribution algorithm via adaptive clustering

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