Competitive Coevolutionary Approach Research Articles

A Competitive Co-Evolutionary Approach for the Multi-Objective Evolutionary Algorithms

In multi-objective evolutionary algorithms (MOEAs), convergence and diversity are two basic issues and keeping a balance between them plays a vital role. There are several studies that have attempted to address this problem, but this is still an open challenge. It is thus the purpose of this research to develop a dual-population competitive co-evolutionary approach to improving the balance between convergence and diversity. We utilize two populations to solve separate tasks. The first population uses Pareto-based ranking scheme to achieve better convergence, and the second one tries to guarantee population diversity via the use of a decomposition-based method. Next, by operating a competitive mechanism to combine the two populations, we create a new one with a view to having both characteristics (i.e. convergence and diversity). The proposed method’s performance is measured by the renowned benchmarks of multi-objective optimization problems (MOPs) using the hypervolume (HV) and the inverted generational distance (IGD) metrics. Experimental results show that the proposed method outperforms cutting-edge co-evolutionary algorithms with a robust performance.

Nash–Cournot equilibrium of a deregulated electricity market using competitive coevolutionary algorithms

In this paper, a competitive coevolutionary approach is used to find out the Nash–Cournot equilibrium of an oligopolistic electricity market based on either centralized or bilateral structure, where the transmission constraints are considered. We consider a market game played by bounded evolutionary agents, adapting their strategies to maximize their profits in a competitive environment. Under the effect of the competition, the coevolved agents are led toward an equilibrium point where each agent population is invaded by an Evolutionary stable Strategy.

Distributed evolutionary optimization, in Manifold: Rosenbrock's function case study

A competitive coevolutionary approach using loosely coupled genetic algorithms is proposed for a distributed optimization of Rosenbrock's function. The computational scheme is a coevolutionary system of agents with only local interaction among them, without any central synchronization. We use a recently developed coordination language, called Manifold, to implement our distributed optimization algorithm. We show that this implementation outperforms a sequential optimization algorithm based on standard genetic algorithms.