An Improved Parent-Centric Mutation With Normalized Neighborhoods for Inducing Niching Behavior in Differential Evolution

Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

doi:10.1109/tcyb.2013.2292971

Cited by 105 publications

(32 citation statements)

References 28 publications

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“…In order to avoid the time complexity caused by pairwise distance calculations, the work [27] proposes a fast niching technique by introducing the locality sensitive hashing, which is an efficient algorithm for approximately retrieving nearest neighbors. An improved information-sharing mechanism among individuals is introduced in [28] to induce more stable and efficient niching behavior, and a newly proposed parent-centric mutation operator is combined with a synchronous crowding replacement rule in [29]. Besides niching methods, new paths to gain the multimodal optimization ability are established.…”

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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“…Other recently proposed DE niching variants include parent centric mutation strategies combined with crowding [58], and ensembles of niching techniques such as speciation [59].…”

Section: B Differential Evolutionmentioning

confidence: 99%

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

Epitropakis

Deb

et al. 2017

IEEE Trans. Evol. Computat.

231

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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“…Several subsequent studies analyzed and developed the niching methods in realm of GAs (Mahfoud, 1995;Sareni and Krahenbuhl, 1998;Mengshoel and Goldberg, 2008). Similar or new niching strategies were also incorporated to other metaheuristics such as particle swarm optimization (PSO) (Qu et al, 2013;Schoeman and Engelbrecht, 2010) and differential evolution (DE) (Basak et al, 2013;Biswas et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations

Ahrari

Deb

Preuß

2017

Evolutionary Computation

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During the recent decades, many niching methods have been proposed and empirically verified on some available test problems. They often rely on some particular assumptions associated with the distribution, shape, and size of the basins, which can seldom be made in practical optimization problems. This study utilizes several existing concepts and techniques, such as taboo points, normalized Mahalanobis distance, and the Ursem's hill-valley function in order to develop a new tool for multimodal optimization, which does not make any of these assumptions. In the proposed method, several subpopulations explore the search space in parallel. Offspring of a subpopulation are forced to maintain a sufficient distance to the center of fitter subpopulations and the previously identified basins, which are marked as taboo points. The taboo points repel the subpopulation to prevent convergence to the same basin. A strategy to update the repelling power of the taboo points is proposed to address the challenge of basins of dissimilar size. The local shape of a basin is also approximated by the distribution of the subpopulation members converging to that basin. The proposed niching strategy is incorporated into the covariance matrix self-adaptation evolution strategy (CMSA-ES), a potent global optimization method. The resultant method, called the covariance matrix self-adaptation with repelling subpopulations (RS-CMSA), is assessed and compared to several state-of-the-art niching methods on a standard test suite for multimodal optimization. An organized procedure for parameter setting is followed which assumes a rough estimation of the desired/expected number of minima available. Performance sensitivity to the accuracy of this estimation is also studied by introducing the concept of robust mean peak ratio. Based on the numerical results using the available and the introduced performance measures, RS-CMSA emerges as the most successful method when robustness and efficiency are considered at the same time.

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An Improved Parent-Centric Mutation With Normalized Neighborhoods for Inducing Niching Behavior in Differential Evolution

Cited by 105 publications

References 28 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

Multimodal Optimization by Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations

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