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In this paper degradation mechanism is discussed to deal with the problem of adaptive population size in evolutionary algorithm. A new evolutionary algorithm based on the degradation mechanism of population size is proposed according to a simplified strategy. The experimental results show that the new algorithm has better performance than an evolutionary algorithm with constant size of population.
When solving complex function optimization problem, Differential evolution(DE) algorithms may suffer from low convergence rate. In this paper, we propose an improved differential evolution algorithm named n-IDE. Our algorithm uses Gaussian sequence to dynamically generate zoom factors and applies an improved hybrid mutation strategy to individuals in order to improve the overall performance. We compare...
The particle swarm optimization (PSO) is an algorithm that attempts to search for better solution in the solution space by attracting particles to converge toward a particle with the best fitness. PSO is typically troubled with the problems of trapping in local optimum and premature convergence. In order to overcome both problems, we propose an improved PSO algorithm that is applied mutation operator...
In this paper, The improved Particle Swarm Optimization in dynamic objective function environment (DOFPSO) is purposed. The dynamic environment will change with the time t. The DOFPSO algorithm discuss that how to determine changes of the time (environment) and how to keep population diversity. The improved algorithm has the ability to fast response the change of environment and could find the best...
A short-term Unit Commitment problem in power systems requires methods that are simple, stable and reconfigurable. A Replicator Dynamics based algorithm can be a good candidate in this regard. In this paper, this method is extended to model the unit specific constraints on the fitness function directly and precisely such that the method retains the optimality of the solution. Furthermore, the results...
This paper proposes an improved differential evolution algorithm, named I-DE, for constrained nonlinear mixed integer programming problems. The new population initialization technology and dynamic non-linear scaling factor are applied to enhance optimization capability of algorithm. We strengthen influence of constraint matrix to deal with constraint of problems. Introduction of special truncation...
This paper provides a detailed description of a novel multivariant optimization algorithm (MOA) for multi-modal optimization with the main idea to share search information by organizing all search atoms into a special designed structure. Its multiple and variant group property make MOA capable on multi-modal optimization problems. The capability of the MOA method in locating and maintaining multi...
In this paper, by combination of some approaches we propose a new approach of Differential Evolution (DE) algorithm, called DE with nonlinear simplex method and dynamic neighborhood search (DENNS). In our approach the nonlinear simplex method (NSM) is used for population initialization and local neighborhood search. Moreover, local and global neighborhood search operators are employed to generate...
Focused on the problem of the QoS global optimal dynamic service selection, this paper established a multi-objective service composition optimization model with QoS restriction. Then, it analyzed some disadvantages of the traditional multi-objective particle swarm algorithm, such as less diversity of solutions and falling into local extremum easily. At last, it proposed a method of chaotic mutation...
Recently, Particle Swarm Optimizer (PSO) has become a popular tool for solving constrained optimization problems. However, there is no guarantee that PSO will perform consistently well for all problems and will not be trapped in local optima. In this paper, a PSO algorithm is introduced that uses two new mechanisms, the first one to maintain a better balance between intensification and diversification...
Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them.We analyze the propagation of memes in MMAs with spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate...
A modified differential evolution (DE) algorithm based on opposition based learning and chaotic sequence named Opposition based Chaotic Differential Evolution (OCDE) is proposed. The proposed OCDE algorithm is different from basic DE in two aspects. First is the generation of initial population, which follows Opposition Based Learning (OBL) rules; and the second is: dynamic adaption of scaling factor...
One of the noticeable topics in fuzzy logic controllers is parameter controllingof heuristic search algorithms. In this paper, one of the parameters of Gravitational Search Algorithm, GSA, is controlledusing fuzzy logic controller to achieve better optimization results and to increase convergence rate. Several experiments are performed and results are compared with the results of the original GSA...
Artificial Bee Colony(ABC) algorithm is a biological-inspired optimization algorithm, which has been shown to be compared with some conventional biological-inspired algorithms, such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Differential Evolution(DE). However, there exists problems such as premature convergence and trapping in local optimal. Inspired by DE, we propose an improved...
In this paper, the vehicle routing problem with fuzzy demands is studied, and a chance constrain programming model under the conditions of fuzzy information is built. Then, an improved tabu search algorithm is proposed for finding the vehicle routing with the lowest total mileage. In the selection of initial solution, a roulette heuristic algorithm is adopted to reduce the search range. The results...
Finding the minimum MPR set is a NP-complete problem in OLSR protocol, and intelligent computing methods can be used to solve it. Based on analyzing the defects of the strategy of the greedy heuristic algorithm, ant colony algorithm is imported to solve the minimum set of MPR problem. Firstly, defining the out-degree and the in-degree of a node, and in accordance with the out-degree and in-degree...
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