DeepAI AI Chat
Log In Sign Up

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

by   Moslema Jahan, et al.

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.


Solving Linear Equations Using a Jacobi Based Time-Variant Adaptive Hybrid Evolutionary Algorithm

Large set of linear equations, especially for sparse and structured coef...

Evolutionary computation for multicomponent problems: opportunities and future directions

Over the past 30 years many researchers in the field of evolutionary com...

Browser-based distributed evolutionary computation: performance and scaling behavior

The challenge of ad-hoc computing is to find the way of taking advantage...

Distributed Evolutionary Computation using REST

This paper analises distributed evolutionary computation based on the Re...