Artificial Bee Colony Algorithm Based On New Search Strategy And Its Application

Intelligent optimization algorithms have been applied in multiple fields,such as function optimization,multi-objective optimization,image processing,etc.Artificial bee colony algorithm(ABC)is one of the intelligent optimization algorithms,which has the characteristics of simple operation,easy implementation,and strong robustness.However,artificial bee colony algorithm has the disadvantages of slow convergence speed and weak local search ability.Therefore,this paper focuses on the improvement of artificial bee swarm algorithm in search strategy,and applies it to function optimization and 0-1 knapsack problem solving.The main results are as follows:(1)To solve the problem of insufficient collaboration caused by single population exchange of artificial bee colony algorithm,artificial bee colony algorithm based on refracted opposition-based learning(CRABC)was proposed by introducing differential mutation search strategy and refracted opposition-based learning search strategy.First,the differential variation and refracted opposition-based learning mechanism is introduced,then puts forward the corresponding search strategy and incorporates it into the artificial bee colony algorithm.Finally,the performance of CRABC algorithm is verified through experiments on 12 benchmark test functions.The experimental results show that the search strategy used is helpful to improve the algorithm performance,and the CRABC algorithm improves the solution quality and convergence speed.(2)Aiming at the disadvantage that artificial algorithm has strong global search ability but is easy to fall into local optimal,artificial bee colony algorithm based on convergence factor and covariance mutation(LWABC)was proposed.The search strategies of the employed bee and onlooker bee are improved to balance the search and development capabilities of the algorithm.First,the convergence factor is introduced into the search equation of the employed bee,and then determine whether to perform covariance mutation on the optimal solution through probability selection during onlooker bee stage.The mutation operation can improve the algorithm’s ability to jump out of the local optimal solution The performance of the LWABC algorithm was verified through experiments on benchmark test functions(3)In the field of discrete optimization,an improved discrete artificial bee colony algorithm(GDABC)is proposed to solve the 0-1 backpack problem.On the one hand,the GDABC algorithm improves the search equation by introducing differential variation and convergence factor to avoid unnecessary search.On the other hand,it combines greedy repair and optimization algorithm to repair the infeasible solution and optimize the candidate solution so as to make the domain search more efficient.The experimental results show that the GDABC algorithm improves the solution quality.