Research On Bio-inspired Intelligent Optimization Algorithm And Its Applications

Optimization problems widely exist in scientific and engineering fields. It has been a hot topicthat how to design effective models and algorithms to solve these optimization problems. Forexample, the WTA problems in the research field of ECM. It is a classical combination problem.This paper tries to construct optimization model for the WTA problem of cooperative jammingand solve it through bio-inspired intelligent optimization method.It needs research in theory and in application about these bio-inspired intelligent optimizationalgorithms. Many problems which exist in the algorithms such as IIGA for the WTA applicationneed to be overcome. Among which, the researchers mainly pay close attention to the followingthree aspects:(1) The universality and robustness of the algorithms remain need to be enhanced.(2) The ductility of the algorithms is insufficient, the performance decreases quickly with thedimension of the problem increases.(3) It needs further research that how to apply existingalgorithm to engineering optimization problems. Based on the analysis of previous work, wehave done some research work on the algorithms and their applications. The mainly researchresults are summarized as follows:(1) Enhanced self-adaptive evolution algorithmGreedy breeding operator, strategy selecting operator, X-evolution operator, populationdiversity maintaining operator, strategy learning operator are designed to consist the algorithmstructure. Besides, the algorithm uses many effective candidate solution generation strategies(CSGSes). An improved probability model is adopted to describe the probability of a strategybeing used to update an antibody. A self-adaptive learning mechanism is introduced into thealgorithm, so the selection probability of each strategy can learning from its experience ingenerating new individuals. Experimental results demonstrate that the proposed algorithm ismore effective than its competitors, and the new operators, multiple solution generatingstrategies and self-adaptive mechanism are effective for generating better solutions.(2) Ensemble evolution algorithm with self-adaptive learning techniquesAccording to the battlefield situation is easily change and the scale of the weapon and thetarget increasing day by day. Thus, an ensemble evolution algorithm with self-adaptive learningtechniques is proposed. The algorithm integrates many population based stochastic searchalgorithms in parallel manner. Different from the proposed algorithm in (1), this algorithmmainly focuses on ensemble of the sub-algorithms which with self-adaptive learning mechanismand make them evolve the sub-algorithms in effective manner. In the proposed algorithm, thepopulation is divided into three sub-populations and the sub-algorithms are employed to evolvethe sub-populations in parallel manner, respectively. We have designed many differentinformation exchange manners (IEMs). Then, many experiments have been done. We have found that the information exchange direction should be from the sub-population which with the globalbest individual at current generation to the sub-population(s) without, the direction should not bepredefined and should be self-adaptive. Experimental results demonstrate that the proposedalgorithm with better performance of robustness and universality.(3) Cooperative jamming decision making based on heuristic self-adaptive discrete differentialevolution algorithmAn optimization model of cooperative jamming decision problem is proposed based onmulti-index jamming effect comprehensive evaluation method for the military operations taskprogramming problem of multiple UCAVs (Unmanned Combat Aerial Vehicles) confrontmultiple threaten radars. In order to solve the model effectively, a Heuristic Self-adaptiveDiscrete Differential Evolution (H-SDDE) algorithm is proposed. In the algorithm, threatendegree based extensional integer coding scheme, heuristic individual adjust operator andindividual repair process are designed. The experimental results indicate that the proposedalgorithm is better than its competitors.(4) Research on strategy selecting of self-adaptive discrete differential evolution algorithmThe Self-adaptive Discrete Differential Evolution (SaDDE) is proposed to solve theCooperative Jamming Weapon-Target Assignment (CJWTA) problem. Obviously, the strategypool plays a significant role in the SaDDE algorithm. First, the Relative Permutation Order basedScale Method (RPOSM) is introduced. The analytic hierarchy process is improved by theRPOSM. Then, the RPOSM based analytic hierarchy process (RPOSM-AHP) is proposed for thestrategy selecting problem. Finally, a feasible scheme is given to solve the strategy selectionproblem by combining the theory and experimental results.