Abstract
To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.
Highlights
As many optimization problems cannot be solved by the traditional mathematical programming methods, the metaheuristic algorithms have been widely used to obtain global optimum solutions
In order to overcome the limited performance of standard krill herd optimization algorithm (KH) on complex problems, a novel free search krill herd algorithm is proposed in this paper
In order to overcome the shortcomings of krill herd algorithm
Summary
As many optimization problems cannot be solved by the traditional mathematical programming methods, the metaheuristic algorithms have been widely used to obtain global optimum solutions. Time interval (Ct) should be fine-tuned in the KH algorithm which is a remarkable advantage in comparison with other natureinspired algorithms It can be efficient for many optimization and engineering problems. Wang proposed a new improved metaheuristic simulated annealing-based krill herd (SKH) method for global optimization tasks [23]. In order to overcome the limited performance of standard KH on complex problems, a novel free search krill herd algorithm is proposed in this paper. The free search strategy has been introduced into the standard KH to avoid all krill individuals getting trapped into the local optima.
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