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This article proposes a discrete particle swarm optimization (DPSO) for solution of the shortest path problem (SPP). The proposed DPSO adopts a new solution mapping which incorporates a graph decomposition and random selection of priority value. The purpose of this mapping is to reduce the searching space of the particles, leading to a better solution. Detailed descriptions of the new solution and...
Geometric constraint problem is equivalent to the problem of solving a set of nonlinear equations substantially. The constraint problem can be transformed to an optimization problem. We can solve the problem by an improved PSO algorithm (IPSO), which is based on the “alldifferent” constraint. It combines the particle swarm optimization algorithm with genetic operators together effectively. When a...
The Distributed Sobol particle swarm optimization (DSPSO) algorithm was studies for solving optimal power flow problem (OPF), in this paper. In the proposed method, swarm size of the particles is separated in multi-groups and searching procedure is divided according with the swarm group. Reducing search space and high cost elimination are concluded in the DSPSO. The DSPSO was tested for solving two...
In this paper, we explore the applicability of quantum-behaved particle swarm optimization (QPSO) algorithm, an efficient variant of particle swarm optimization (PSO) algorithm, to online system identification problems. First, quantum particle swarm optimization and particle swarm optimization are introduced. Then these two algorithms and genetic algorithms are applied to online identify parameters...
SPICE model is one of the key technical connections between the integrated-circuit technology community and the design community. Design community requires accurate SPICE model parameters so as to make the difference between design spec and practical spec minimized as possible. To get accurate model parameters, optimization algorithms are used for parameter extraction. A parallel hybrid evolutionary...
Resource and project optimization scheduling has become the key of the success of researching project in the enterprises. In order to solve the mass resource constrained project scheduling problem, in this paper, an improved particle swarm algorithm (PSO) called particle swarm algorithm with crossover (CPSO) was presented. This improved algorithm is based on PSO and genetic algorithm (GA). Through...
This paper presents a novel particle swarm optimizer combined with roulette selection operator to solve the economic load dispatch (ELD) problem of thermal generators of a power system. Several factors such as quadratic cost functions with valve point loading, transmission loss, generator ramp rate limits and prohibited operating zone are considered in the computation models. This new approach provides...
Cognitive radio (CR) has become a hotspot in recent research. We can think of a CR as having three main parts: the ability to sense, the capacity to learn, and the capability to adapt. Adaptation to the outside environment to optimize radio parameters has been previously proposed using genetic algorithms (GA) to select the optimal transmission parameters by scoring a subset of parameters and evolving...
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...
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,...
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