Yanmin Shang

An Improved Parallel Adaptive Genetic Algorithm Based on Pareto Front for Multi-objective Problems

For multi-objective optimization problems, we introduced IPAGA (Improved Parallel Adaptive Genetic Algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this Algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The results show that the parallel genetic algorithm developed in this paper is efficient.

An improved genetic algorithm based on J 1 triangulation and fixed point theory

An improved genetic algorithm based on J1 triangulation is proposed for multimodal optimization problems. And the fixed point theory is introduced into this improved algorithm. The optimal problems are conversed to fixed point problems. In this paper, several typical functions are used to demonstrate the effectiveness of this algorithm, and the testing results show that the improved genetic algorithm is valid and highly effective.

An Improved Multi-Objective Adaptive Niche Genetic Algorithm Based On Pareto Front

For multi-objective optimization problems, an improved multi-objective adaptive niche genetic algorithm based on Pareto Front is proposed in this paper. In this Algorithm, the rank value and the niche value are introduced to evaluate the individuals. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The experimental results show that this algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.

An Improve Genetic Algorithm Based on Fixed Point Algorithms

An improved genetic algorithm is proposed to solve optimal problems, which is based on fixed point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on a simplicial subdivision of searching space and generates the integer labels at the vertices, then, applied crossover operators and increasing dimension operators according to these labels. In this case, it is used as an objective convergence criterion and termination criterion that the labels of every individual are completely labeled simplexes. The algorithm combines genetic algorithms with fixed point algorithms and triangulation theory to maintain the proper diversity, stability and convergence of the population. Several numerical examples are provided to be examined and the numerical results illustrate that the proposed algorithm has higher global optimization capability, computing efficiency and stronger stability than traditional numerical optimization methods and the standard genetic algorithm.

An Improved Genetic Algorithm Based on Fixed Point Theory for Function Optimization

This paper introduces triangulation theory into genetic algorithm and with which, the optimization problem will be translated into a fixed point problem. An improved genetic algorithm is proposed by virtue of the concept of relative coordinates genetic coding, designs corresponding crossover and mutation operator. Through genetic algorithms to overcome the triangulation of the shortcomings of human grade, it can start from any point to find the most advantages. Gradually fine mesh will be introduced the idea of genetic algorithms so that the search area gradually decreased, improving the efficiency of search. Finally, examples demonstrate the effectiveness of this method.

An Improved Multi-Objective Genetic Algorithm Based On Pareto Front and Fixed Point Theory

For multi-objective optimization problems, an improved multi-objective genetic algorithm based on Pareto Front and Fixed Point Theory is proposed in this paper. In this Algorithm, the fixed point theory is introduced to multi-objective optimization questions and K1 triangulation is carried on to solutions for the weighting function constructed by all subfunctions, so the optimal problems are transferred to fixed point problems. The non-dominated-set is constructed by the method of exclusion. The experimental results show that this improved genetic algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution. Keywords— multi-objective optimization; Pareto Front; nondominated set; genetic algorithm; fixed point; K1 triangulation

An Improved Genetic Algorithm Based on hK1 Triangulation

An improved genetic algorithm based on the hK1 triangulation is proposed for optimization of dual multimodal function. With this algorithm, the optimal problems converse to solution of fixed point problems. The minimum points can be distinguished by using the Hessian Matrix. The test results of many typical functions indicated that the algorithm is valid and highly effective.

An Improved Multi-Objective Adaptive Genetic Algorithm Based on Pareto Front

For multi-objective optimization problems, an improved multi-objective adaptive genetic algorithm based on Pareto Front is proposed in this paper. In this Algorithm, the non-dominated-set is constructed by the method of exclusion.The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according to solution quality. The experimental results show that this algorithm convergent faster and is able to achieve a broader distribution of the Pareto optimal solution.

An Improved Genetic Algorithm Based on J1 Subdivision and Fixed Point Theory

In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on a J1 subdivision of searching space and generates the integer labels at the vertices, The nature reproduction by fission phenomenon introduction genetic algorithm, proposes one kind of improvement genetic algorithm, This algorithm has avoided searching the solution space repeated and achieved the optimal solution. The result illustrate that the proposed algorithm have global optimization capability, computing efficiency and strong stability.

An Improved Genetic Algorithm Based on Subdivision Theory

In this paper an improved genetic algorithm is proposed to solve optimal problems applying triangulation theory of continuous self-mapping in Euclidean space. The algorithm operates on a simplicial subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and increasing dimension operators relying on the integer labels are designed. In this case, whether each individual is a completely labeled simplex can be used as an objective convergence criterion and that determined whether the algorithm will be terminated. The algorithm combines genetic algorithms with subdivision theory, maintaining the proper diversity, stability and convergence of the population. Finally, several numerical examples are provided to be examined. Numerical results indicate that the proposed algorithm has higher global optimization capability, computing efficiency and stronger stability than traditional numerical optimization methods and standard genetic algorithms.