A Study On Strategy Optimization Of The Whale Optimization Algorithm And Its Application

Whale optimization algorithm is a swarm intelligence algorithm created by simulating the hunting behavior of humpback whales.It has the characteristics of strong universality,low computation cost and few control parameters,and is an efficient algorithm.WOA provides many feasible solutions to solve complex optimization problems with large scale and multiple constraints in real life.However,the theoretical model and application of WOA are still in the development stage,and there are some problems such as easy to fall into local optimal,slow convergence rate and insufficient exploration ability.To solve these problems,this thesis proposes two kinds of whale algorithm for strategy optimization,which are improved and applied to test set function optimization and Wireless Sensor Networks(WSN)area coverage.The details are as follows:(1)A population feedback adaptive weight with whale algorithm(PAWOA)is proposed,which adopts a relatively novel refraction reverse learning mechanism in the population initialization stage.It is used to optimize the initial position of whale population in the search space and accelerate the convergence rate of the algorithm.The adaptive weight size was adjusted by feedbacks of the actual updates of the population in the optimization process of the algorithm,and the head-whale weighting strategy was adopted to enhance the optimization ability of the algorithm.At the late stage of the algorithm,the diversity of whale population is scarce,so a random golden sinusoidal guidance mechanism is proposed.The global random individuals were used to guide the search path of the population based on the golden ratio coefficient sine operator,and the greedy preference strategy was introduced to update the population position to strengthen the ability of the algorithm to jump out of the local optimal.Fifteen benchmarking set function optimization questions were used to verify PAWOA’s performance,which was compared with other swarm intelligence algorithms and well-known improved whale algorithms in recent years.The experimental results show that PAWOA has better searching ability and faster convergence rate.(2)An improved whale algorithm with circle exploration(CEIWOA)is proposed.SPM chaotic mapping with more uniform initial distribution and higher ergodicity was used to initialize the population,which was convenient for optimization.The subsection adjustable convergence factor is introduced to balance the exploration and production ability of the algorithm.A circular exploration method with a wider search area is proposed to improve the problem of insufficient exploration ability of the algorithm.Finally,Levy flight strategy with strong search ability is introduced to avoid premature convergence.The performance of CEIWOA is verified in the practical application of WSN area coverage optimization problem,and compared with the well-known whale variant algorithm and other swarm intelligence algorithm in the research direction of WSN area coverage.The results show that CEIWOA has better coverage,better node deployment and faster optimization speed.