Dynamic parameterization of the particle swarm optimization and genetic algorithm hybrids for vehicle routing problem with time window

Abstract

Particle Swarm Optimization (PSO) is a well known technique for solving various kinds of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem. Many techniques have been proposed for alleviating this problem. One of the alternative approaches is hybridization. Genetic Algorithms (GAs) are one of the possible techniques used for hybridization. Most often, a mutation scheme is added to the PSO, but some applications of crossover have been added more recently. Some of these schemes use dynamic parameterization when applying the GA operators. In this work, dynamic parameterized mutation and crossover operators are combined with a PSO implementation individually and in combination to test the effectiveness of these additions. The results indicate that all the PSO hybrids with dynamic probability have shown satisfactory performance in finding the best distance of the Vehicle Routing Problem With Time Windows.

Comparison of genetic algorithm components and selection variants in unlawful behavior detection of hand movement

“…This paper presents and implemented an unlawful behavior detection system by adapting genetic algorithm (GA) [45] to efficiently detect the unlawful hand movement, which may lead to unlawful entry. To measure the performance of the system, the confusion matrix derivation of the system for both GA TOS and RPS was calculated.…”

The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

“…Several algorithms based on the three-implementation framework with different parameter setting have been tested on different datasets of facility layout problem. The results have indicated some improvements from the PSO-GA [33] hybrids with dynamic parameterizations compared to the constant parameterization. As these experiments only focuses on the SG scheme, the future works should extend the evaluations to other kinds of implementation frameworks that have been introduced in this paper.…”

“…The α is bounded within 0.1 times of the particle dimension. The algorithms have been previously tested on several benchmark functions [25][26] and the Vehicle Routing Problem with Time Windows (VRPTW) [28]. At this time, the interest has been coined to test the algorithms in Facility Layout Problem (FLP) [29].…”

Optimal capacitor sizing and placement based on real time analysis using PSO for energy efficiency improvement

“…Particle Swarm Optimization (PSO) [14] technique and also to reduce the UiTM S&T Tower electricity bill.…”