Distributed Evolutionary Computation: A New Technique for Solving Large Number of Equations

TL;DR

This paper introduces a distributed evolutionary computation method that decomposes large problems into smaller parts, utilizing a Jacobi-based adaptive algorithm and new selection methods to efficiently find optimal solutions with significant speedup.

Contribution

The paper presents a novel distributed evolutionary algorithm with cluster computation, including new selection methods, to solve large-scale problems more efficiently than traditional approaches.

Findings

Achieves optimal solutions for large parameter problems.

Significant speedup in distributed system performance.

Effective decomposition of decision vectors enhances efficiency.

Abstract

Evolutionary computation techniques have mostly been used to solve various optimization and learning problems successfully. Evolutionary algorithm is more effective to gain optimal solution(s) to solve complex problems than traditional methods. In case of problems with large set of parameters, evolutionary computation technique incurs a huge computational burden for a single processing unit. Taking this limitation into account, this paper presents a new distributed evolutionary computation technique, which decomposes decision vectors into smaller components and achieves optimal solution in a short time. In this technique, a Jacobi-based Time Variant Adaptive (JBTVA) Hybrid Evolutionary Algorithm is distributed incorporating cluster computation. Moreover, two new selection methods named Best All Selection (BAS) and Twin Selection (TS) are introduced for selecting best fit solution vector. Experimental results show that optimal solution is achieved for different kinds of problems having huge parameters and a considerable speedup is obtained in proposed distributed system.