Activity: Talk or presentation types › Invited talk
Metaheuristic algorithms are popular for solving search and optimization problems. The applications of metaheuristic algorithms are wide and cover several areas such as optimization, pattern recognition, learning, scheduling, economics, bioinformatics, natural language processing, image, and video processing etc. Objective (single or multi) function is used to measure the effectiveness of a metaheuristic algorithm, distributed randomly in the population. Typically, a population start dominating as the searching evolves. During the searching, objective function value shows no improvement as the population converges to a local optimum, this leads to the problems of the premature convergence and slow finishing. There are several factors involves for the success of a metaheuristic algorithm. In this talk, I will cover (a) the factors which needs special attention before implementing a metaheuristic algorithm for engineering optimization problems; (b) premature convergence and slow finishing; (c) approaches for alleviating premature convergence; and (d) a case study to explain the methodology of implementing a metaheuristic algorithm for solving a real-world optimization problem.