Variable Optimization Problems In The Design Of Hybrid Particle Swarm Algorithm Oriented Research

Particle swarm optimization is a swam-intelligence-based algorithm,whichsimulates the animal social behavior in the nature.Different from otherevolutionary optimization algorithms,it employs not only the positioninformation,but also the velocity information to control the particles’trajectories.The algorithm model is simple and easy to implement, and strongability of self-organization, self-adaptation and self-study.It has been applied tomany engineering fields.Particle swarm optimization is a littleapplication in thefield of constrained optimization. The application of particle swarm algorithm isnot a lot in the field of constrained optimization,especially for the mixedvariable constrained optimization problem, the studies based on particle swarmoptimization are not many relative to other intelligent algorithms.Therefore,how to use the simple and efficient advantage of the particle swarmalgorithm to solve mixed variable constrained optimization problem isbecoming a research direction of the current optimization design.In thispaper,the PSO used to solve the mixed variable optimization problem in thepaper[58] can’t be consistent with the optimal solution, different improvementsare proposed to improve the accuracy of solutions and get the optimalsolution.The main research in this paper includes the following aspects:1) Using the MPSO algorithm in the literature [58] as a global searchalgorithm and simulated annealing algorithm (SA) as a local searchalgorithm,proposed different hybrid intelligent optimization method to solve themixed variable optimization problems. MPSO can obtain the values of thenon-continuous variables very well for mixed-variable optimization problems.However, the imprecise values of continuous variables brought the inconsistentresults of each run. On the basis of the MPSO algorithm, using the simulatorannealing algorithm and PSO algorithmfor the continuous variables of optimalsolution to the accuracy of search after the MPSO algorithm finishes eachindependent run, in order to obtain the consistent optimal results formixed-variable optimization problems. 2) Proposed improved PSO algorithm with constraint-preservingmechanism (referred to as CPMPSO) for solving mixed-variable optimizationproblem, it usingconstraint-preserving mechanism combined with the PSOalgorithm as a global search algorithm and PSO as a local search algorithm. Thevalues of non-continuous variables are got according to the direction and size ofthe particle velocity, the constraint-preserving method is used as the mechanismfor handling the constraint violations, and the particle swarm optimization itselfis used as local search algorithm to obtain the consistent optimal results formixed-variable optimization problems.