Two Kinds Of New Intelligent Optimization Algorithms With Conjugate Gradient Operator

By using some traditional optimization algorithms,such as the steepest descentmethod, Newton method, conjugate gradient method,some simple optimization questionscan be solved.However,with the more and more complicated optimization questionsappeared,the traditional optimization algorithm are not enough to solve these problems.Inthe past decades,with the development of the computer technology,people have putforward many intelligent optimization algorithms to solve these high dimension,nonlinear, nondifferentiable optimization problems,such as particle swarm optimizationalgorithm,genetic algorithm, simulated annealing algorithm,differential evolutionalgorithm.Any kind of algorithms requires a constant improvement process,so as to solveall kinds of practical problems.Therefore,based on the existing algorithms,the improvedalgorithm and the new algorithm are very important.A new global optimization algorithm called explosion search algorithm is proposedby basing on the bomb or firework explosion scene and another new global optimizationalgorithm called cloud search algorithm is proposed by blending the natural phenomenaof clouds.In order to improve the local search ability of the algorithm,a traditionalalgorithm which is called conjugate gradient algorithm,was added in the two kinds ofnew intelligent optimization algorithms.Considering that in some optimizationproblems,the derivative of the objective function can not be obtained,therefore in theprocess of introducing the conjugate gradient method，using the difference quotient toreplace the derivative,the method is called approximate conjugate gradient method.Basedon the above,proposed two kinds of novel intelligent optimization algorithms withconjugate gradient operator,namely improved Explosion Search Algorithm withConjugate Gradient Operator and improved Cloud Search Optimization Algorithm withConjugate Gradient Operator.Specifically,In CGESA,introduced a new mutation operator to enhance the global search ability. Meanwhile,introduced a new operatornamely conjugate gradient operator to improve the local search ability of the optimalburst point.In CGCSO algorithm,introduced a new operator namely conjugate gradientoperator to improve the local search ability of the optimal water,so that CGCSOalgorithm’s convergence speed and optimization precision are improved.Experimentalresults of some conventional benchmark functions indicate that CGESA achieves betterperformance than ESA,and that CGCSO algorithm performs better than the original CSOalgorithm,especially for the unimodal functions.