Scripting language constructs for dynamic parameterizations of PSO-GA hybrids

Abstract

Responding to the difficulties of implementing meta-heuristics hybridization, this paper introduces a set of scripting language constructs for the rapid algorithm design and development focusing on the two well-known metaheuristics, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, the PSO-GA hybrids have been embedded with dynamic parameterization. In this paper, the compiler specification and codes for developing the scripting language constructs are described. Then, based on the several algorithms of PSO-GA hybrids that have been developed with the scripting language constructs, the Line of Codes (LOC) are measured in order to test the easiness of the programming language. The results show that across all algorithms, the scripting language is anticipated to enable easy programming, which has been presented by the very less of LOC compared to the JAVA programming language.