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Genetic algorithm get local optimal solution too easily and converge slowly on selection problem of mobile IP routing, in order to solve the optimization problem, an approach based on quantum-behaved particle swarm optimization algorithm (QPSO) is proposed, the approach take advantages of quantum computation such as strongly parallel calculating abilities, global convergence and rapidly operating...
Adaptive resource allocation is one of the most challenging tasks for multiuser orthogonal frequency division multiplexing (OFDM) systems. In this paper, two evolutionary approaches, genetic algorithm (GA) and particle swarm optimization (PSO) have been applied for adaptive subcarrier and bit allocations to minimize the overall transmit power of a multiuser OFDM system. Each user will be assigned...
Bacterial foraging optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO). This paper first analyzes...
Engineering design studies can often be cast in terms of optimization problems. However, for such an approach to be worthwhile, designers must be content that the optimization approaches employed is fast convergence. Usefulness of heuristic algorithm as the search method for diverse optimization problems is examined. Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural...
A comparative study between genetic algorithm and particle swarm optimization in FIR filter design is presented in this paper. FIR filter design involves multi-parameter optimization, on which the existing optimization algorithms donpsilat work efficiently. Given the filter specification to be designed, both algorithms generate a set of filter coefficients and try to meet the ideal frequency characteristic...
Searching is an important procedure in optimization problems. As is an effective clustering method especially in spatial data mining, the role of searching is essential. While many searching methods focus themselves on particle swarm optimization and genetic algorithms, we propose a new searching algorithm based differential evolution (DE). It proves that DE is a simple optimization algorithm effective...
This paper explores a comparative performance study of two new classes of particle swarm optimization (PSO) techniques and binary coded genetic algorithm (GA) applied to the optimization of proportional-integral-derivative (PID) gains of PID-controlled automatic voltage regulator (AVR). The two novel swarm optimization techniques are velocity update relaxation particle swarm optimization (VURPSO)...
A new global path planning approach based on binary particle swarm optimization algorithm (BPSO) for a mobile robot is presented. The detailed realization of the approach is illustrated. The obstacles in the robot's environment are described as polygons and the vertexes of obstacles are numbered from 1 to n. Binary particle swarm optimization is used to plan the path. The length of the particle is...
Fuzzy particle swarm optimization (FPSO) has shown its great searching ability and high computing precision, while it can not assure the algorithm is convergent. In this paper, a new kind of FPSO is proposed, called convergent fuzzy particle swarm optimization (CFPSO), employing the convergent gene. It differs from normal FPSO in that a convergent gene is introduced in the velocity equation. And it...
The first hitting time (FHT) plays an important role in convergence evaluation for evolutionary algorithms. However, the current criteria of the FHT are mostly under a hypothesis that never has been testified: the FHT subjects to the normal distribution. Aiming at more convincible evaluations, this paper investigates the distribution of the FHT through a goodness-of-fit test and discovers an unexpected...
This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time,...
In this paper, a novel evolutionary algorithm (EA) with two groups is presented based on the mimicry of a two-group team for a specific objective. The operations of exploration and epitome-based learning behaviors are properly defined. By means of the inherited generation of new individual and the replacement rules of the team, the behavior division between the elite group and the plain group is established,...
This paper presents Brent method for solving economic dispatch problem with transmission losses. The algorithm involves selection of lambda values then the optimal lambda is evaluated from the power balance equation by Brent method. The proposed algorithm has been tested on a power system having 3, 6, 15 and 20 generating units and the simulation results were compared in terms of their solution quality,...
In this paper, a new approach using independent component analysis (ica) and hybrid Flexible Neural Tree (FNT) is put forward for face recognition. To improve the quality of the face images, a series of image pre-processing techniques, which include histogram equalization, edge detection and geometrical transformation are used. The ICA based on Kernel principal component analysis (KPCA) and FastICA...
In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance...
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