Research On Optimization Algorithm Based On Fishing Strategy

Optimization methods have important application value in engineering design and so on. So to obtain new optimization algorithms combining fishing strategy optimization algorithm with other optimization methods that are used to solve complex unconstrained and constrained problems is of great research significance.The main contents of this paper are as follows:(1) This paper simply introduces the basic concept, behavior description, algorithm implementation and process of fishing strategy optimization algorithm. As a novel optimization method, fishing strategy optimization algorithm is also similar with other intelligent algorithms, owns shortcomings that perform as slow convergence speed, easily fall into local optimum and so on.(2) In order to avoid the shortcoming of FSOA that easily fall into the local optimum, we introduce the traditional optimization methods such as Powell algorithm, Hooke-Jeeves algorithm and artificial fish school algorithm, and embed them into the fishing strategy optimization methods, then propose three hybrid optimization methods. Simulation results show that the three kinds of hybrid optimization algorithm methods have faster convergence speed and better global search capability.(3) Making use of the faster convergence speed of differential evolution algorithm and the high accuracy of fishing strategy optimization algorithm, we combine differential evolution algorithm with fishing strategy optimization algorithm, so as to balance the capability of global search and local optimization .Simulation results show that the hybrid algorithm has higher accuracy and better stability, and provides a more effective new way to solve the constrained optimization problem.