Research And Applications Of Fruit Fly Optimization Algorithm Based On Cloud Model

Swarm intelligence is a recent trend in computational intelligence and popular for the simplicity of its realizations and effectiveness of solving complex optimization problems.Fruit fly optimization algorithm(FOA)is a global swarm intelligence optimization algorithm inspired by the foraging behavior of fruit fly swarm.To improve the convergence performance of FOA,several improved methods based on cloud model are proposed in this paper.Moreover,the improved FOA is extended to multi-objective optimization problems,and the performance of the proposed methods is verified by real-world engineering optimization problems.The main results and contributions of this dissertation are as follows:The research background of fruit fly optimization algorithm is introduced firstly.Then,a review of the development of FOA is presented.Furthermore,the basic FOA is introduced in detail,and the pseudocode of FOA is given.Thereafter,the definition of cloud model and the normal cloud generator are also provided.To improve the global search ability and solution accuracy of the FOA,a bimodal fruit fly optimization algorithm using normal cloud learning(BCMFOA)is proposed in this paper.Based on the labor allocation characteristics of the swarm foraging behavior,the fruit fly population is divided into two groups in the optimization process according to their duties of searching or capturing.Moreover,the randomness and fuzziness of the foraging behavior of fruit fly swarm are described by a normal cloud model.Therefore,the ability of FOA to avoid local optima is enhanced greatly.Twenty-three benchmark functions are used to test the performance of the proposed BCMFOA algorithm.Numerical results show that the proposed method can significantly improve the global search ability and solution accuracy of FOA.Since,the candidate solution generation mechanism based on smell concentration judgment value has the disadvantage of getting into the local optima and the optimal solution cannot be negative,a normal cloud model based fly optimization algorithm(CMFOA)is proposed in this paper.The randomness and fuzziness of the foraging behavior of fruit fly swarm in osphresis phase is described by the normal cloud model.Moreover,an adaptive parameter strategy for Entropy En in normal cloud model is adopted to improve the global search ability in the early stage and to improve the accuracy of solution in the last stage.Thirty-three benchmark functions are used to test the effectiveness of the proposed method.Numerical results show that the proposed CMFOA can obtain better or competitive performance for most test functions.To deal with multi-objective optimization problems,the Pareto domination concept is integrated into the selection process of the fruit fly optimization and a cloud model based multi-objective fruit fly optimization algorithm(MOCMFOA)is then developed.Similar to most of other multi-objective evolutionary algorithms,an external elitist archive is utilized to preserve the non-dominated solutions found so far during the evolution,and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive.The WFG and CEC2009 multi-objective optimization problems are used to test the performance of the proposed MOCMFOA.Numerical results show that MOCMFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions.To further test the performance of the proposed methods for real-world engineering optimization.The CMFOA is applied to estimate multi-parameters of a permanent magnet synchronous motor(PMSM)system and the MOCMFOA is applied to multi-objective optimization design of heat pipe shape for an artificial satellite and speed reducer design problem.The experimental results confirm their good performance in real-world engineering practices.