A Weighted Spider Monkey Optimization Algorithm

Swarm intelligence algorithms have their own advantages and disadvantages.As a new algorithm,Spider Monkey Optimization(SMO)algorithm has been proved to be very flexible in the category based on swarm intelligence algorithm.With the help of numerical experiments on test problems,it has been shown that for most problems,SMO algorithm can get better results than Particle Swarm Optimization(PSO)algorithm?Artificial Bee Colony Optimization(ABC)algorithm and other algorithms in terms of reliability(success rate),efficiency(average function evaluation times)and accuracy(average objective function value).Therefore,we have reason to believe that SMO algorithm will have a good development prospect in the field of optimization algorithms based on swarm intelligence.Spider Monkey Optimization(SMO)algorithm is a new type of swarm intelligence algorithm with great development potential,but there are still many shortcomings.Spider monkey algorithm has slow convergence speed,poor robustness and it is easy to fall into local extremum.In this paper,the spider monkey algorithm with a linear decreasing inertia weight is proposed for the problem.In the local leader stage and the local leader decision stage,a linearly decreasing inertia weight is introduced to the original position of the spider monkey,which promotes the algorithm to globally search the solution space.This method also greatly increases the diversity of the spider monkey population preventing the algorithm from sinking into local optimal solutions that may be encountered during the iterative process and the local mining capability of the WSMO algorithm can accelerate the convergence.The numerical experiments with six benchmark functions show that the improved algorithm is better than the original spider monkey algorithm in terms of convergence speed,optimization precision and robustness.Particularly,the improvement of the performance of the original spider monkey algorithm is more significant when solving the multimodal function optimization problem.