Research On The Improvement And Application Of Artificial Bee Colony Algorithm

Artificial Bee Colony algorithm (ABC) proposed in2005is one of the current bestevolutionary algorithms, which has become the research hotspot in many fields such asevolutionary computing and intelligent optimization. At present, ABC has successfully beenapplied to diverse domains of science and engineering, such as neural network optimization,filter design, cognitive radio, and blind signal separation. However, almost all of theevolutionary algorithms, including ABC, still suffer from the problems of prematureconvergence, slow convergence rate and difficult parameter setting, especially in optimizinghigh-dimensional complex optimization problems. In addition, the standard ABC algorithmcan’t be used directly to solve the multimodal function optimization problems and thisshortcoming limits the scope of application of ABC to some extent.According to the insufficiency of ABC, it is deeply investigated from theory andapplication aspects in this paper. In theory, according to a series of optimization problems,including high-dimensional complex single objective optimization problem, multimodalfunction optimization, two objective optimization problem, many objective optimizationproblem and constrained multi-objective optimization problems, the structure and key steps ofthe algorithm are improved to improve its optimal performance in every optimization problem.In application, the improved ABC algorithm is applied successfully to solve a frontiercoverage-all targets problem for directional sensor networks based on three-dimensionalperception, Concrete contents is as follows.Firstly, according to ABC still suffer from the problems of premature convergence, slowconvergence rate and slow convergence speed at a later time in optimizing high-dimensionalcomplex single objective optimization problem, the inherent operation mechanism of ABCare deeply investigated. An improved artificial bee colony algorithm was proposed to improvethe optimization performance. Concrete improvement measures in the improved ABCalgorithm include:1、considering the method of choosing the excellent individual ofemployed bees is too greedy, a new probability choice model is proposed to increasepopulation diversity;2、A new searching method is designed, in which the better individualsare utilized to guide the search direction synchronously, to ensure the population diversity andimprove convergence speed;3、considering the parameter of controlling the behavior of the scout bees is difficult to set and has a greater impact on the algorithm, the new searchingmodel of scouts is proposed. Experimental results show that the proposed algorithmoutperform several state-of-the-art optimization algorithms in terms of the main performanceindexes.Secondly, in order to improve multimodal evolutionary algorithms, a niche artificial beecolony algorithm is proposed combining ABC and the niche technology based on a lot ofexperiments. On the one hand, the traditional niche model is improved to increase populationdiversity and enhance the capacity of identifying every peak. On the other hand, according tomultimodal optimization problems, concrete improvement measures in NABC include:1、considering the method of choosing the excellent individual of employed bees is too greedy, anew probability choice model is proposed to increase population diversity;2、the traditionalevaluation criteria of judging superiority and inferiority individual depending on individualfitness value is improved, a new evaluation method combining the niche technology isproposed to strengthen the searching ability of individuals in every peak;3、in order to avoidthe phenomenon of losing the peak points because of population diversity deficiency, theexternal population is established to record the acquired peak points. Experimental resultsshow that the proposed algorithm can identify each peak accurately.Thirdly, in order to improve the performance of convergence and distribution ofmulti-objective evolutionary algorithms, a multi-objective optimization algorithm based onartificial bee colony algorithm is proposed, in which NSGA-II is taken as the main frameworkof two targets evolutionary algorithm and evolutionary operation is implemented by ABC.Concrete improvement measures in the proposed algorithm include:1、the method ofascertaining elite population is designed to improve the distribution of the optimal solutionsets;2、according to characteristic of two targets optimization problems, new searchingmethod is designed to accelerate the converges speed to the optimal Pareto front.Experimental results on ZDT show that, the proposed algorithm can get Pareto optimalsolutions effectively with good distribution performance, all of its performance indexes arebetter than or at least comparable to several existing state-of-the-art MOEAs.Fourthly, in order to improve the performance of many objective evolutionary algorithms,a many objective evolutionary algorithm based on artificial bee colony algorithm is proposedin this paper. Concrete improvement measures in the proposed algorithm include:1、many objective optimization problem is transformed to single objective optimization problem toincrease the power of convergence;2、 according to characteristic of many objectiveoptimization problems, new searching method is designed to form an improved ABC, andevolutionary operation is implemented by an improved ABC;3、a new diversity maintenancemethod is established to improve distributivity performance. Experimental results show that,the proposed algorithm can get Pareto optimal solutions effectively with good distribution andconvergence performance and with a wide coverage area.Fifthly, considering that the performance of constrained multi-objective evolutionaryalgorithms, a constrained multi-objective optimization algorithm based on ABC is proposedin this paper. Concrete improvement measures in the proposed algorithm include:firstly,external populations are constructed to store feasible solutions and infeasible solutionsrespectively to handle constraint conditions, the update method of feasible solution set isimproved to distribution of solution set effectively. Secondly, ABC is utilized as theevolutionary strategy, new searching strategy is proposed, in which the excellent feasible andinfeasible solutions are utilized to improve exploration ability. Experimental results on CTPtest functions demonstrate that the proposed algorithm can achieve better diversity of Paretosolutions and convergence performance than or at least comparable to several existingstate-of-the-art CMOEAs.Sixthly, in order to solve coverage-all targets for wireless directional sensor networksbased on three-dimensional perception model, a universal coverage-all targets algorithm isproposed. On the one hand, the present three-dimensional perception model is improved, andthe calculation formula of the optimal elevation angle is derivate by deep mathematicalanalysis, which is solved by an improved ABC. On the other hand, the mathematical model ofallocation scheme of deviation angle is established to reduce the complexity, and which issolved by an improved ABC. Experimental results show that this algorithm can solvecoverage-all targets efficiently.