Abstract
Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.
Highlights
Nowadays, real-world optimization problems are complex with high dimensional search space they are in general challenging
Dynamic Group-based Cooperative Optimization Algorithm (DGCO) divides the individuals into two groups: the exploration group and the exploitation group
In order to evaluate the performance of the proposed optimization algorithm, twenty-three standard benchmark mathematical functions have been used to find their minimum values in a specific domain of the search space
Summary
Real-world optimization problems are complex with high dimensional search space they are in general challenging. Optimization refers to finding an acceptable optimal solution for a specific problem among many feasible ones. An optimization task is usually transformed into a search problem in a multi-dimensional space. That search refers to minimizing or maximizing an objective function that evaluates the quality of a solution candidate that is usually denoted by a vector in the search space. Metaheuristics are a family of approximate optimization techniques that provide acceptable solutions in a reasonable time [11]. They are adopted for solving hard and complex problems in science and engineering
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