Particle swarm optimization with crossover: a review and empirical analysis

Abstract

Since its inception in 1995, many improvements to the original particle swarm optimization (PSO) algorithm have been developed. This paper reviews one class of such PSO variations, i.e. PSO algorithms that make use of crossover operators. The review is supplemented with a more extensive sensitivity analysis of the crossover PSO algorithms than provided in the original publications. Two adaptations of a parent-centric crossover PSO algorithm are provided, resulting in improvements with respect to solution accuracy compared to the original parentcentric PSO algorithms. The paper then provides an extensive empirical analysis on a large benchmark of minimization problems, with the objective to identify those crossover PSO algorithms that perform best with respect to accuracy, success rate, and efficiency.

Multiobjective Reliable Cloud Storage with Its Particle Swarm Optimization Algorithm

“…Hence, during the evolution process of the designed PSO algorithm, a tournament based selection strategy is employed to select particle to be updated. So as to ensure the convergence of the designed PSO algorithm, we employ the elitism strategy to select the next population from the offspring and parents and follow the guidance in [36,41,43].…”

“…PSO has found a wide range of applications in function optimization, neural network, fuzzy systems, and many other fields [39][40][41]. An extensive survey of PSO applications has been made by Poli and Engelbrecht [42,43]. In [36,41], several variations of PSO algorithms for multiobjective optimization problems are introduced and their convergence is also analyzed.…”

“…In [36,41], several variations of PSO algorithms for multiobjective optimization problems are introduced and their convergence is also analyzed. Also, the convergence of PSO algorithm had been studied empirically in [43,44]. Thus, according to the guidance and analysis in Mathematical Problems in Engineering (1) Randomly generate an initial swarm (2) while termination criterion is not satisfied do (3) for each particle do (4) if the particle is better than then (5) ← position of (6) end if (7) = min ( neighbours ) (8)…”

Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement

“…Zhou [22] introduced two mechanisms, namely competitive group optimization and reverse learning, choosing different learning mechanisms according to fitness value, and proposed a reverse learning competitive PSO algorithm. Engelbrecht [23] proposed a dynamic PSO algorithm based on arithmetic crossover. Chen [24] used two different crossover operations to disseminate promising samples through the crossover of the optimal position of each particle's personal history to establish an effective guiding paradigm and maintain good diversity.…”

Fabricação e modelagem de uma nova geometria para espectrometria de mobilidade iônica de tolueno, propanol e água com elevada relação sinal-ruído.

“…Optimization algorithms based on the Swarm Intelligence (SI) technique have been used in applications such as: solution for planning or scheduling, artificial neural network (ANN) optimization parameters, data mining, control systems and in several other applications in the field of science and engineering. The SI is based on the social behavior of flocks of birds, bees, ants and others, as a way to find in nature inspiration for algorithms with the lowest possible complexity, which aim to find the optimal solution for multidimensional problems [8][9][10][11][12]. Particle swarm optimization (PSO)…”