Background and Motivation
The most recently, many meta-heuristic optimization techniques that are conceptually different from the traditional mathematical programming techniques have been developed in order to solve the large-scale, mixed-integer, non-linear and non-convex optimization problems (Lee & El-Sharkawi, 2008). This is because of the superiority of the non-traditional meta-heuristic optimization techniques in comparison with traditional mathematical programming techniques, when feasible area of the solution space and or dimensions of the optimization problem increases (Lee & El-Sharkawi, 2008). The non-traditional meta-heuristic optimization techniques have been inspired by certain attributes and behavior of biological, swarm of fauna, and neurobiological systems. The most popular meta-heuristic algorithms can be classified into genetic algorithm (GA) (Holland, 1975), particle swarm optimization (PSO) (Kennedy & Eberhart), simulated annealing (SA) (Kirkpatrick, Gelatt & Vecchi, 1983), Tabu search (TS) (Glover, 1977), ant colony optimization (ACO) (Dorigo, Maniezzo & Colorni, 1996), artificial bee colony (ABC) (Karaboga, 2005), artificial fish-swarm (AFS) (Li, Shao & Qian, 2002), bacterial foraging optimization (BFO) (Passino, 2002), bat algorithm (BA) (Yang, 2010), cuckoo search (CS) (Yang & Deb, 2009), firefly algorithm (FA) (Yang, 2009), etc. As further elucidation, the details of these algorithms are tabulated in Table 1. These studies into the meta-heuristic optimization techniques show that most of aforementioned algorithms are employed only for solving a specific class of convex and non-convex optimization problems. This is because of the fact that the performance of these meta-heuristic algorithms depend on a confined solution space. In other word, in every new generation, a new set of vectors is produced by using randomized selection and improved operators from the limited set of vectors. Therefore, these meta-heuristic algorithms cannot maintain their proper performance by increasing the irregular dimensions of real-world large-scale optimization problems.
As a result, researchers and engineers in different area of sciences are enthusiastic to use innovative alternatives in the optimization techniques, to improve the performance and efficiency of solving the real-word large-scale optimization problems.