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The Research on Multi-Objective Optimization Problem Based on the Improved Shuffle Frog Leaping Algorithm

Author: WangXiaoDi
Tutor: XiaoWei
School: Hunan Normal University
Course: Theory of computer software
Keywords: Frog leaping algorithm Genetic Operators Grouping method Multi-objective optimization Knapsack problem
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 148
Quote: 2
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Abstract


In reality, the process, the optimization of the problem is often accompanied by the constraints of the target , and these are usually still multi-objective optimization problem , and the need for simultaneous optimization of multiple targets , which often speak of multi-objective optimization problem (Multi-Objective Optimization Problem , MOP). At the moment, to solve multi-objective optimization problem there are many, are basically intelligent use of evolutionary optimization methods . This paper describes the shuffled frog leaping algorithm (Shuffle Frog Leaping Algorithm, SFLA) this emerging group of intelligent optimization algorithm , and is described together with the classic genetic algorithm (Genetic Algorithms, GA). In both algorithms based on the algorithm for Leapfrog proposed genetic - frog leaping algorithm (Genetic-Shuffle Frog Leaping Algorithm, G-SFLA). On the one hand , in the subgroup of leapfrog algorithm into the evolution of genetic algorithm genetic operators , through the sub- group within the optimal solution and the worst solution crossover to produce new solutions if better than the worst solution is to replace the original worst solution , otherwise with the entire population of the optimal solution and the worst solution crossover book group generated new solutions if better than the worst solution is to replace the original worst solution , if it can not get a book group had the worst solution than the better solution this mutation causes the worst solution to generate new solutions instead of the original worst solution ; hand leapfrog algorithm is proposed to improve the original grouping method , ie on the basis of the original grouping methods each have joined other groups outside of the group itself, in addition to random of an individual to form a new group. Finally, multi-objective 0-1 knapsack problem as an example verify the improved algorithm Leapfrog Leapfrog algorithm than the original benefits in performance and improved by way of example leapfrog algorithm with different parameters to do some research .

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