Abstract
Seagull optimization algorithm (SOA) inspired by the migration and attack behavior of seagulls in nature is used to solve the global optimization problem. However, like other well-known metaheuristic algorithms, SOA has low computational accuracy and premature convergence. Therefore, in the current work, these problems are solved by proposing the modified version of SOA. This paper proposes a novel hybrid algorithm, called whale optimization with seagull algorithm (WSOA), for solving global optimization problems. The main reason is that the spiral attack prey of seagulls is very similar to the predation behavior of whale bubble net, and the WOA has strong global search ability. Therefore, firstly, this paper combines WOA’s contraction surrounding mechanism with SOA’s spiral attack behavior to improve the calculation accuracy of SOA. Secondly, the levy flight strategy is introduced into the search formula of SOA, which can effectively avoid premature convergence of algorithms and balance exploration and exploitation among algorithms more effectively. In order to evaluate the effectiveness of solving global optimization problems, 25 benchmark test functions are tested, and WSOA is compared with seven famous metaheuristic algorithms. Statistical analysis and results comparison show that WSOA has obvious advantages compared with other algorithms. Finally, four engineering examples are tested with the proposed algorithm, and the effectiveness and feasibility of WSOA are verified.
Highlights
Over the past ten years, the metaheuristic algorithm has become very popular
Genetic Algorithm (GA) [1] is a random search algorithm, which mainly imitates the natural selection in the biological world. e main reason for the success of GA is that the behaviors of selection, replication, and mutation are random, which can help the algorithm avoid falling into local optimum
25 benchmark functions are used to test the performance of the proposed WSOA. is section is divided into five parts
Summary
Over the past ten years, the metaheuristic algorithm has become very popular. For example, it has been widely used to find solutions to many complex problems in engineering and computer science. e main reasons for its popularity are flexibility, gradient-free mechanism, and avoiding falling into local optimization. E main reasons for its popularity are flexibility, gradient-free mechanism, and avoiding falling into local optimization. Metaheuristic algorithms only need to look at the input and output to consider optimization problems and do not need to calculate the derivative of search space, which makes them more flexible in solving various optimization problems. Genetic Algorithm (GA) [1] is a random search algorithm, which mainly imitates the natural selection in the biological world. E main reason for the success of GA is that the behaviors of selection, replication, and mutation are random, which can help the algorithm avoid falling into local optimum. Grey Wolf Optimization (GWO) algorithm [4] simulates the hierarchical mechanism and predation behaviors of grey wolf population in nature. Squirrel Search Algorithm (SqSA) [7] is mainly inspired by squirrel’s foraging behavior
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