NDLI: An improved crowding-based differential evolution for multimodal optimization

Content Provider	IEEE Xplore Digital Library
Author	Li Chen Lixin Ding
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China (Li Chen; Lixin Ding)
Abstract	Traditional optimization technologies usually try to find a global optimum, however, many optimization problems are multimodal with many global or local optima. In real world, multiple optima are usually interested in and can give people multi-choices. Crowding-based differential evolution (CRDE) algorithm is a simple but very powerful for multimodal optimization. CRDE has good explorative ability to find the optima in search space. The main shortcoming of CRDE is the convergence speed is low. To welcome this, an improved CRDE with local search on the individuals nearest optima in the population is introduced. Local search uses Gaussian mutation whose mutation range decreases linearly with iteration. It makes refined search in the area around the optima and improves the exploitable ability. To identify the best individuals around the optima in the current population, the idea of specifying the seeds of species (i.e. the best individuals in niches) in species-based particle swam optimization (SPSO) is adapted. The introduced algorithm is tested on multimodal benchmark problems CRDE used and the test shows it outperforms CRDE in convergence speed greatly.
Starting Page	1973
Ending Page	1977
File Size	287427
Page Count	5
File Format	PDF
ISBN	9781424481620
e-ISBN	9781424481651
DOI	10.1109/ICECENG.2011.6057739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Local search Accuracy Crowding scheme Differential Evolution Evolutionary computation Euclidean distance Benchmark testing Multimodal optimization Optimization Convergence Genetic algorithms
Content Type	Text
Resource Type	Article

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