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Artificial Colony Algorithm (ABC) is a random optimization algorithm based on swarm search behavior, which is widely used in recent years. Considering the slow convergence and ease of falling into the local optimum of basic ABC, researchers try various modified methods to overcome its shortcomings. Different modified ABC algorithms have different characteristics and application scope. In order to...
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...
An improved Particle Swarm Optimization (IPSO) algorithm is proposed in this paper. In the algorithm, a premature estimate mechanism is introduced to judge whether the particles accumulate in a small region and tell the probability whether the swarm is trapped in a local optimum. If the estimate criterion is satisfied, the chaotic mutation operation, which makes use of the chaos search strategy and...
The existing chaos optimization algorithms were almost based on Logistic map. However, the probability density function of chaotic sequences for Logistic map is a Chebyshev type function, which may affect the global searching capacity and computational efficiency of chaos optimization algorithm. In this paper, firstly, a new chaotic sequences with Skew Tent map (STM) is established, and is improved...
A parallel chaos optimization algorithm based on probability selection is proposed to resolve the function optimization problems. The searching space divides into origin space and elaborate space. During the optimization, the two spaces are searched synchronously according to different probability. The boundary of the elaborate space is decreased continuously, and its searching probability is increased...
The paper presents a new hybrid global optimization algorithm based on chaos search and complex method for nonlinear constrained optimization problems. To fit for chaos optimization algorithm, a constrained optimization problem is transformed into an unconstrained problem by penalty function method. The mapping mode of standard complex method is improved to solve the problem of low computational efficiency...
Particle swarm optimization (PSO) is a good optimization algorithm, but it always premature convergence to local optimization, especially in some complex issues like optimization of high-dimensional function. In this paper, a particle swarm optimization based on chaotic neighborhood search (PSOCNS) is proposed. When the sign of premature convergence is arise, search each small area which is defined...
A novel dynamic particle swarm optimization algorithm based on chaotic mutation (DCPSO) is proposed to solve the problem of the premature and low precision of the common PSO. Combined with linear decreasing inertia weight, a kind of convergence factor is proposed based on the variance of the populationpsilas fitness in order to adjust ability of the local search and global search; The chaotic mutation...
A novel immune quantum evolutionary algorithm based on chaotic searching for global optimization (CRIQEA) is proposed. Firstly, by niching methods population is divided into subpopulations automatically. Secondly, by using immune and catastrophe operator each subpopulation can obtain optimal solutions. Because of the quantum evolutionary algorithm with intrinsic parallelism it can maintain quite nicely...
Particle swarm optimization (PSO) algorithm is frequently employed to solve various optimization problems, but it easily gets into the local extremum in later evolution period. An improved chaos search strategy is introduced into PSO algorithm. When particles get into the local extremum, they are activated by chaos search strategy, and chaos search area are controlled in the neighborhood of the current...
The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. A chaotic optimization-based simple particle swarm optimization equation with constriction factor is developed. Piecewise linear chaotic map is employed to perform chaotic optimization...
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