Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Abstract

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Chaotic Quantum Double Delta Swarm Algorithm Using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

“…As we continue to further our understanding of how emergent properties arise out of simple, local-level interactions at the lowest hierarchical levels, we may expect the evolutionary computation community to increasingly consider scale-free interactions among atomic agents on top of the existing, already rich body of research on biomimicry. The proposed paradigm is well-suited for application in single-objective unimodal/multimodal optimization problems such as those discussed in [8,[13][14]30] along the lines of digital filtering, fuzzy-clustering, scheduling, routing etc. The QDDS and subsequently C-QDDS approaches build on a growing corpus of algorithms hybridizing quantum swarm intelligence and global optimization and adds to the existing collection of nature-inspired optimization techniques.…”

“…We choose the constant k to be 5 and θ to be the product of a random number drawn from a zero-mean Gaussian distribution with a standard deviation of 0.5 and a factor of the order of 10 -3 after sufficient number of trials. The learning rate χ decreases linearly with iterations from 1 to 0.3 according to equation (24) as an LTV weight [8]. ℎ ℎ ∈ [0,1] is a random number generated using a Chebyshev chaotic map in equation (27).…”

A review of deep learning with special emphasis on architectures, applications and recent trends

“…In addition to this, to speed up the process the authors randomly picked up partial datasets for evaluation. Thus several variants of PSO along with its hybridised versions [125] as well as a host of recent swarm intelligence algorithms such as Quantum Double Delta Swarm Algorithm (QDDS) [126] and its chaotic implementation [127] proposed by Sengupta et al can be used, among others for automatic generation of architectures used in Deep Learning applications.…”

Data-Driven Optimization of Public Transit Schedule

“…Particle Swarm Optimization Eberhert and Kennedy [14] proposed particle swarm optimization (PSO) as a stochastic population based optimization algorithm which can work with non-differentiable objective function without explicitly assuming its underlying gradient disparate from gradient descent techniques. The interested reader is directed to [21] by Sengupta et al for a detailed understanding of the algorithm. PSO has been shown to satisfactorily provide solutions to a wide array of complex real-life engineering problems, usually out of scope of deterministic algorithms [8][5] [3].…”