Modified Shuffled Frog Leaping Algorithms And The Application Of Multi-objectives Optimization

In the practical problems, the problems are not only a single objective function.But It is necessary to find optimum solutions in the multiple objective functions. Wecalled it Multi-Objective Optimization Problem. Multi-Objective OptimizationProblem is the research field that a lot of investigator pay through the nose for theproblem. Nowadays, in the engineering and different scientific areas, a lot ofMulti-Objective Optimization Problem become key factors for researcher to transformthat into a single objective Optimization Problem. But it have a lot of defect that therelationship between a objective function and another one. Or the defect is thedifferent system of units between a objective function and another one. Now we canuse Genetic Algorithms, Particle Swarm Optimization, Ant Colony Algorithm,Simulated Annealing, Predatory Search and so on to solve Multi-ObjectiveOptimization Problem. The article use SFLA to solve the Multi-ObjectiveOptimization Problem, we called it MOSFLA. I make the work of QuantumMechanics to SFLA, we called it Q-SFLA. The theory describe the status of frogsusing wave function. According to Schrodinger equation we can find the densityfunction in the space. Then on the basis of Monte Carlo method we can find theposition of frogs, and the update of strategy. Then I combine SFLA with GeneticAlgorithms. Genetic Algorithms contain Chiasma and Variation. The frogs needsorting and grouping. But in the MOSFLA it is very difficult to solve the problem ofsorting. Because it have a lot of solutions that they are not controlled by another one.We can use a method of classification. We can make the same solution of the sameclass and we can assort it on the basis of the number of maximal group. At last, wecan apply MOSFLA to Knapsack Problem of Multi-Objective Optimization Problem.I give the eloquent of MOSFLAand make a sharp contrast with MOPSO.The main contributions can be listed as follows: Optimization problem includetraditional optimization methods and optimization of intelligence methods. Thegeneration and development are expatiated, and Multi-objective optimization problem of present situation，evolution and theory are researched. The deficiency of SFLAthatcontain backdrop，development，theory，process，preferences and astringency so forthis researched and analysed. Then Particle Swarm Optimization (PSO) and GeneticAlgorithms (GA) are studied in order to compare with SFLA. On the basis of theshortcoming of the ability of search and convergent velocity SFLA need improve, andthe methods are that making SFLAand Multi-objective optimization problem together.Multi-objective optimization problem of SFLA of Quantum mechanics is propoundedand Making SFLA and GA together. At last I make a lot of experiments aboutKnapsack problem and Optimize proportion of a little loan in order to provefeasibility of the theory and come to a conclusion.