Constrained Optimization Problems Solving Using Evolutionary Algorithms: A Review

Solving Constrained Optimization Problems (COPs) is challenging task in the field of computer optimization. Many researchers have put efforts to solve COPs using techniques such as Dynamic Programming, Non Linear Programming etc. These methods are generally trapped in local optima. The solution to this lacuna is Evolutionary Algorithms (EAs), which work as a promising technique for wide range of Constrained Optimization Problems. This paper reviews established Evolutionary Algorithms specifically, Genetic Algorithm (GA), Artificial Bee Colony (ABC), Differential Evolution (DE), Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO) and variants of above Evolutionary algorithms which have solved Constrained Optimization Problems. This review will help new researchers to know about various Evolutionary Algorithms and their potential strengths and weaknesses to solve COPs.