A quarter century of particle swarm optimization

Abstract

Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, the particle swarm optimization algorithm has attracted many researchers' attention. Through the convergent operation and divergent operation, individuals in PSO group and diverge in the search space/objective space. In this paper, the historical development, the state-of-the-art, and the applications of the PSO algorithms are reviewed. In addition, the characteristics and issues of the PSO algorithm are also discussed from the evolution and learning perspectives. Every individual in the PSO algorithm learns from itself and another particle with good fitness value. The search performance and convergence speed were affected by different learning strategies. The scheduling and data-mining problems are illustrated as two typical cases of PSO algorithm solving real-world application problems. With the analysis of different evolution and learning strategies, particle swarm optimization algorithm could be utilized on solving more real-world application problems effectively, and the strength and limitation of various PSO algorithms could be revealed.

An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS

“…However, urban areas are typically composed of impermeable surfaces and bare lands, which increase storm water runoff. Therefore, considering the importance of this factor, high-resolution image obtained from the WorldView-3 satellite was used to extract an LULC map [40]. The WorldView-3 satellite is a high-spectral-and high-resolution satellite imagery.…”

“…PSO describes the solution of each optimization issue as a bird, which searches one space "particle". In general, PSO is modified to a class of unsystematic particles used to explore ideal answers using iterative techniques [40]. Thus, for each iteration, particles communicate by tracing excesses of position and velocity.…”

A Proposal for the Organisational Measure in Intelligent Systems

“…Several algorithms which imitate the behaviour of those intelligent systems have been proposed. This article focuses on some of the most used: Ant-based algorithms [14,15], Particle Swarm Optimisation [16,17], Artificial Swarm Fish Algorithm [18], Artificial Bee Colonies [19,20] and Firefly Algorithm [21,22]. Some recent articles that analyse publications related to swarm methods show that the selected methods are among those considered in more publications [1,7].…”

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

“…Many methods of EAs have been suggested to deal with such problems [13,14]. This optimization problem has also been considered by different heuristic methods such as; tabu search [15][16][17], simulated annealing [18][19][20], memetic algorithms [11], differential evolution [21,22], particle swarm optimization [23,24], ant colony optimization [25], variable neighborhood search [26], scatter search [27,28] and hybrid approaches [29][30][31]. Multiple applications in various areas such as computer engineering, computer science, economic, engineering, computational science and medicine can be expressed or redefined as problem in Equation (1), see [2,32] and references therein.…”