The implementation frameworks of meta-heuristics hybridization with dynamic parameterization

Abstract

The hybridization of meta-heuristics algorithms has achieved a remarkable improvement from the adaptation of dynamic parameterization. This paper proposes a variety of implementation frameworks for the hybridization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) and the dynamic parameterization. In this paper, taxonomy of the PSO-GA with dynamic parameterization is presented to provide a common terminology and classification mechanisms. Based on the taxonomy, thirty implementation frameworks are possible to be adapted. Furthermore, different algorithms that used the implementation frameworks with sequential scheme and dynamic parameterizations approaches are tested in solving a facility layout problem.

Heuristic based model for groceries shopping navigator

“…Sometimes, the shoppers must ask the supermarket staff about the position of the products, which adds more time to the shopping task [4]. Therefore, we need to find an optimal shopping route for such shoppers, which not only can navigate themselves in finding their products inside the supermarket but also can reduce their shopping times.A heuristic technique is an approach used for problem-solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals [5][6]. It is designed to solve a specific problem quickly when the classic methods are too slow [7].…”