Journal Article10.1016/J.AMC.2011.05.051
A sequential niching memetic algorithm for continuous multimodal function optimization
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TL;DR: Performance measurements show that the sequential niching memetic algorithm (SNMA) proposed in this work has very good scalability and outperforms other algorithms in accurately locating multiple optima, both global and local.
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About: This article is published in Applied Mathematics and Computation. The article was published on 01 May 2012. The article focuses on the topics: Memetic algorithm & Fitness landscape.
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Citations
Locating Multiple Optima via Brain Storm Optimization Algorithms
TL;DR: Three variants of brain storm optimization (BSO) algorithms, which include original BSO algorithm, BSO in objective space algorithm with Gaussian random variable, and BSO with Cauchy random variable were utilized to solve multimodal optimization problems in this paper.
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Region-based memetic algorithm with archive for multimodal optimisation
TL;DR: A specially designed memetic algorithm for multimodal optimisation problems that divides the search space in predefined and indexable hypercubes with decreasing size, called regions to keep high diversity in the population and to keep the most promising regions in an external archive.
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Multimodal function optimizations with multiple maximums and multiple minimums using an improved PSO algorithm
TL;DR: A multimodal function optimization problem consisting of multiple maximums and multiple minimums is solved using an improved particle swarm optimization (PSO) algorithm that contains a number of best particles, not a single global best particle.
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Bees Algorithm for multimodal function optimisation
ZD Zhou,YQ Xie,Duc Truong Pham,Silah Hayati Kamsani,Marco Castellani +4 more
- 01 Mar 2016
TL;DR: In the proposed Bees Algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions, and two procedures of radius estimation and optima elitism are added, to enhance the algorithm’s capability to find multiple optima in multimodals optimisation problems.
Generalized pigeon-inspired optimization algorithms
TL;DR: School of Computer Science, Shaanxi Normal University, Xi’an 710119, China'; School of Electronic and Information Engineering, Beihang University, Beijing 100191, China; School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou 221008, China.
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David E. Goldberg,William Shakespeare +1 more
- 01 Jan 2002
TL;DR: The present work expresses the problem as a multi-objective optimization problem and a methodology has been proposed based on multi-objective genetic algo-rithm (MOGA) that exploits the effectiveness of MOGA for searching global optimal solutions in selecting an appropriate image enhancement operator.
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Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
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TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
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