Abstract
The Bare Bones Particle Swarm Optimization (BBPSO), because of its implementation simplicity, has been a popular swarm-based metaheuristic algorithm for solving optimization problems. However, as found in its many variants, their search behaviors were not considered in the design. Instead of employing heuristics, we formulate a low complexity particle swarm optimizer, called the First-Order Bare Bones Particle Swarm Optimizer (FODBB), whose behavior obeys the principle of first-order difference equations. The search trajectory can be constructed in a prescribed manner together with decreasing random searches that enable particles to explore the search space more completely. This characteristic thus allows for a wider search coverage at initial iterations and consequently improves the search performance. A comparative evaluation with recently reported BBPSO algorithms was conducted and experimental results indicate that the proposed optimizer outperforms others in a majority of benchmark optimization functions.
Highlights
The Particle Swarm Optimization (PSO) algorithm is a metaheuristic based optimization approach regarded as efficient and effective in solving many optimization problems [1]
A fitness value decreasing trend that denotes improvements on the optimal solution can be observed from the First-Order Bare Bones Particle Swarm Optimizer (FODBB) fitness plots of these functions
This is attributed to the fact that the sampling Gaussian distribution standard deviation is non-zero, such that particle stagnation is prevented
Summary
The Particle Swarm Optimization (PSO) algorithm is a metaheuristic based optimization approach regarded as efficient and effective in solving many optimization problems [1]. One of the noticeable proposals is the Bare Bones Particle Swarm Optimization (BBPSO) [4] that has become a popular algorithm in the class of swarm-based metaheuristics. It was employed together with the co-evolution strategy in data clustering [8] In another application, BBPSO was modified to handle binary problems and used in data feature selection [9]. R. Li et al.: First-Order Difference Bare Bones Particle Swarm Optimizer metaheuristic algorithms, such as adopting the crossover and mutation operator from the Genetic Algorithm (GA) [13] to maintain search diversity. An enhanced algorithm called First-Order Bare Bones Particle Swarm Optimizer (FODBB) is proposed.
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