Your browser version is outdated. We recommend that you update your browser to the latest version.

Manuscript Title: A Review of Population Based Meta-Heuristic Algorithm

Author : Zahra Baheshti and Siti Mariyam Shamsuddin

Email :bzahra2@live.utm.my,mariyam@utm.com 

Abstract: Exact optimization algorithms are not able to provide an appropriate solution in solving optimization problems with a high-dimensional search space. In these problems, the search space grows exponentially with the problem size therefore; exhaustive search is not practical. Also, classical approximate optimization methods like greedy-based algorithms make several assumptions to solve the problems. Sometimes, the validation of these assumptions is difficult in each problem. Hence, meta-heuristic algorithms which make few or no assumptions about a problem and can search very large spaces of candidate solutions have been extensively developed to solve optimization problems these days. Among these algorithms, population-based meta-heuristic algorithms are proper for global searches due to global exploration and local exploitation ability. In this paper, a survey on meta-heuristic algorithms is performed and several population-based meta-heuristics in continuous (real) and discrete (binary) search spaces are explained in details. This covers design, main algorithm, advantages and disadvantages of the algorithms.

Keywords: Optimization, Meta-heuristic algorithm, Population-based meta-heuristic Algorithm, high dimension search space, Continuous and discrete search spaces.

Vol 5 (1)