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Based on the existing algorithm of fault-sectin location in distributed network containing distributed generation(DG), the effect of localization is not ideal, especially premature convergence problem in the original genetic algorithm, a new fault location method of chaotic optimization based on multiple-population genetic algorithm is proposed. Firstly, the introduction of a number of population...
This paper presents an effective methodology to optimally reconfigure an electrical distribution network. Selective Particle Swarm Optimization (SPSO) algorithm is proposed to find the optimal combination of switches that results in a radial configuration with minimum system power loss. SPSO is a modified Binary Particle Swarm Optimization (BPSO) with selective search space. Comparative analysis of...
A method of chaotic optimization and immune algorithm is presented for service restoration after faults in distribution system in this paper, which might improve the probability for optimal solution, and have the characteristic of chaotic optimization and artificial immunity algorithm. In the proposed algorithm, chaotic optimization is used to initialize the antibody of immune algorithm firstly in...
Multiobjective evolutionary algorithms (MOEAs) that use nondominated sorting have been criticized mainly for their computational complexity and nonelitism approach. In this paper, we suggest a non-dominated sorting based multiobjective evolutionary algorithm (MOEA), called nondominated sorting genetic algorithm-II (NSGA-II) for solving the fault section estimation problem in automated distribution...
In this paper, a bionic algorithm based on Genetic Algorithms is proposed as a varietal GA, named External Self-evolving Multiple-archives (ESMA). ESMA focuses on improving the efficiency of applying diversity for enhancing the solution quality. This paper proposes three mechanisms for self-evolving Multiple-archives, which are Clustering Strategy, Switchable Mutation and Elitist Propagation. These...
In this paper, the performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm. The gradient based optimizer is used for a fast...
This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its...
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