Seeking multiple solutions:an updated survey on niching methods and their applications

Li, Xiaodong and Epitropakis, Michael G. and Deb, Kalyanmoy and Engelbrecht, Andries (2017) Seeking multiple solutions:an updated survey on niching methods and their applications. IEEE Transactions on Evolutionary Computation, 21 (4). pp. 518-538. ISSN 1089-778X

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Abstract

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned 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 most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other 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.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Evolutionary Computation
Additional Information:
©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
ID Code:
83738
Deposited By:
Deposited On:
21 Dec 2016 09:26
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 03:15