Research On Improvement And Application Of Differential Evolution Based On Cloud Model

Differential Evolution (DE) is one of the current best evolutionary algorithms, which hasbecome the research hotspot in many fields such as evolutionary computing and intelligentoptimization. At present, DE has successfully been applied to diverse domains of science andengineering, such as signal processing, neural network optimization, pattern recognition,machine intelligence, chemical engineering and medical science. However, almost all of theevolutionary algorithms, including DE, still suffer from the problems of prematureconvergence, slow convergence rate and difficult parameter setting, especially in optimizingcomplex optimization problems. In addition, the standard DE algorithm can’t be used directlyto solve the multi-objective optimization problems (MOPs) and this shortcoming limits thescope of application of DE to some extent.The article studies DE from theory and application aspects. In theory, firstly, accordingto the insufficiency of DE, the structure and key steps of the algorithm, including mutationand crossover, are deeply investigated and a series of numerical experiments are made in thispaper. For one thing, a new division method of population diversity is proposed, based onwhich a novel adaptive adjustment strategy of parameter CR is presented to improve thepopulation diversity and to avoid sticking at local optima. For another, a new DE mutationstrategy is designed, in which best solutions are utilized to guide the search directionsynchronously, avoiding the search blindness brought by the random selection of individualsin difference vector. So a new modified p-ADE algorithm is presented. Experimental resultsdemonstrate the improved DE can effectively improve the global search ability of DE andoutperform several state-of-the-art optimization algorithms in terms of the main performanceindexes, such as DEGL, JADE, jDE and CLPSO. Secondly, cloud theory is creativelyintroduced to the DE algorithm to construct a new DE version, called CDE. Firstly, a noveladaptive adjustment strategy of parameter CR is proposed based on cloud model, whichcombines the presented adaptive adjustment stratage of parameter CR based on populationdiversity with the characterisitics of stability and randomness of DE. Secondly, CDE utilizes theconnection of positive and inverse normal cloud generators to produce one-dimensionalperturbations to each individual so as to improve population diversity and avoid sticking atlocal optima. Experimental results on benchmark functions demonstrate that CDE can effectively improve the population diversity of DE, avoid sticking at local optima and speedup the convergence rate.In application, CDE is used to solve the constrained multi-objective optimization andurban traffic signal coordination control problems. For the constrained multi-objectiveoptimization problems, firstly, CDE is utilized as the evolutionary strategy, parameter CR isadjusted adaptively by positive normal cloud generator and a novel mutation strategy isproposed, in which the excellent feasible and infeasible solutions are utilized to improveexploration ability. Secondly, external populations are constructed to store feasible solutionsand infeasible solutions respectively to handle constraint conditions, the update method offeasible solution set is improved to distribution of solution set effectively. Experimentalresults on CTP test functions demonstrate that CMODE can achieve better diversity of Paretosolutions and convergence performance. For urban traffic signal coordination control, CDEalgorithm is introduced to optimize urban route two-way traffic signals. The new methodbased on CDE is compared with current best coordination control method based onmulti-colony immune algorithm. Experimental results prove the superiority of CDE onconvergence accuracy, speed and robustness, it could provide the more excellent phasedifference for traffic artery, decrease average delays in straight-going traffic flow and improvecapacity of urban road traffic.