Abstract
This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm. The WDO-DE algorithm is based on a double population evolution strategy, the individuals in a population evolved by wind driven optimization algorithm, and a population of individuals evolved from difference operation. The populations of individuals both in WDO and DE employ an information sharing mechanism to implement coevolution. This paper chose fifteen benchmark functions to have a test. The experimental results show that the proposed algorithm can be feasible in both low-dimensional and high-dimensional cases. Compared to GA-PSO, WDO, DE, PSO, and BA algorithm, the convergence speed and precision of WDO-DE are higher. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm.
Highlights
Nature always makes people produce a lot of inspiration
This paper presents a new hybrid global optimization algorithm, which is based on the wind driven optimization (WDO) and differential evolution (DE), named WDO-DE algorithm
Many natural heuristic algorithms have been proposed for solving real-world and large scale problems which are very difficult to solve by traditional methods, for example, genetic algorithm (GA) [1], particle swarm optimization (PSO) [2, 3], ant colony optimization (ACO) [4], differential evolution (DE) [5], firefly algorithm (FA) [6], bat algorithm (BA) [7], cuckoo search (CS) [8], flower pollination algorithm (FPA) [9], wind driven optimization (WDO) [10]
Summary
Nature always makes people produce a lot of inspiration. In the past, many natural heuristic algorithms have been proposed for solving real-world and large scale problems which are very difficult to solve by traditional methods, for example, genetic algorithm (GA) [1], particle swarm optimization (PSO) [2, 3], ant colony optimization (ACO) [4], differential evolution (DE) [5], firefly algorithm (FA) [6], bat algorithm (BA) [7], cuckoo search (CS) [8], flower pollination algorithm (FPA) [9], wind driven optimization (WDO) [10]. WDO algorithm can suffer from the premature convergence when solving global optimization problems It is an important method for relieving the premature convergence to control the population diversity. In order to overcome the deficiencies of a single algorithm in solving a global optimization problem, in this paper, we propose a new hybrid global optimization algorithm based on the wind driven optimization and differential evolution. This evolution strategy allows WDO and DE algorithms to give full play to their respective advantages. This hybridization showed a better optimization performance and robustness and significantly improves the original WDO algorithm
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