Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by in...

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation ...

This paper presents a combinational quantum-inspired binary gravitational search algorithm (QBGSA) for solving the optimal power quality monitor (PQM) placement problem in power systems for voltage sag assessment. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concept and principles of quantum behaviour as to improve the search capability with ...

This paper presents a dynamic particle swarm optimization based search for optimal fusion configuration of sensors in distributed detection network in presence of a nonstationary binary symmetric channel. The wireless channel in sensor networks is a non-stationary random process, which moves the optima of the original problem, otherwise static. The optimal fusion configuration minimizes the pro...

Feature selection is a useful technique for increasing classification accuracy. The primary objective is to remove irrelevant features in the feature space and identify relevant features. Binary particle swarm optimization (BPSO) has been applied successfully in solving feature selection problem. In this paper, chaotic binary particle swarm optimization (CBPSO) with logistic map for determining...

Particle swarm optimization(PSO) has been applied on feature selection with many improved results. Traditional PSO methods have some drawbacks when dealing with binary space, which may have negative effects on the selection result. In this paper, an algorithm based on fitness proportionate selection binary particle swarm optimization(FPSBPSO) will be discussed in detail aiming to overcome the p...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...

This paper presents a proposal based on binary particle swarm optimization to design combinational logic circuits at the gate-level. The algorithm is validated using several examples from the literature, and is compared against a genetic algorithm (with integer representation), and against human designers who used traditional circuit design aids (e.g., Karnaugh Maps). Results indicate that part...