Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology

By Xiaodong Li. IEEE Transactions on Evolutionary Computation, 2010

489 citations recorded by the source corpus.

Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters, often used to inform a niching algorithm how far apart between two closest optima or the number of optima in the search space, are typically difficult to set as they are problem dependent. This paper describes a simple yet effective niching algorithm, a particle swarm optimization (PSO) algorithm using a ring neighborhood topology, which does not require any niching parameters. A PSO algorithm using the ring topology can operate as a niching algorithm by using individual particles' local memories to form a stable network retaining the best positions found so far, while these particles explore the search space more broadly. Given a reasonably large population uniformly distributed in the search space, PSO algorithms using the ring topology are able to form stable niches across different local neighborhoods, eventually locating multiple global/local optima. The complexity of these niching algorithms is only O(N), where N is the population size. Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require niching parameters.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1e459c242662d78e5e1025fc7731d3b15219f216

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Citations in the corpus

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “[180], rPSO by Bird and Li [181], Dynamic SPSO by Parrot and Li [182] and the lbest PSO with Ring Topology by Li [183] are some of the well-known approaches used in such environments.”
Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, methodology): “In total, 18 different multimodal algorithms are considered in our experiments: 1) NCDE: the neighborhood based crowding DE; 2) NSDE: the neighborhood based speciation DE; 3) NShDE: the neighborhood based sharing DE; 4) CDE [6]: the original crowding DE; 5) SDE [13]: speciation-based DE; 6) ShDE: mo”
Particle swarm optimization algorithm: an overview (2017, background): “Most researches (Bratton and Kennedy 2007; Kennedy andMendes2002;Li2010)usedthestatictopologystructure.”
Bio-inspired computation: Where we stand and what's next (2019, background): “tive proposals to multimodal problems using PSO algorithms have gravitated on the use of multi-swarms [176], the induction of Euclideanbased niching [168] or a ring topology in the neighborhoods within the swarm [177].”
Particle Swarm Methods (2018, background): “The effectiveness of both SPSO and FERPSO has led to further enhancements such as the ones in [71, 118, 125].”
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems (2012, background): “[24], [29], [40], diversity maintenance [3], [4], [6], [21], [34], hybridization with auxiliary operations [1], [32], [46], and adaptive PSO [15], [34], [36], [37], [52], which are briefly reviewed below.”
Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization' (2013, background): “The first 10 benchmark functions are simple, well known and widely used functions, largely based on some recent studies on niching [11], [12], [13].”
A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems (2018, methodology): “In contrast, r3pso described in [12], which incorporates an index-based ring topology, has been demonstrated through experimental results that it can form stable niches.”