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In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
The Group Search Optimizer(GSO) is a novel optimization algorithm, which is inspired by searching behavior of animals. In this paper, we proposed an improved GSO algorithm named Fast Global Group Search Optimizer(FGGSO) to increase searching speed and balance the exploitation and exploration of the algorithm, which is based on our previous works. At first time, considering the complexity and time-consuming...
Shuffled frog leaping algorithm (SFLA) is meta-heuristic for solving complex optimization problems. It is one of promising optimistic methods which are based on swarm intelligence. SFLA combines the advantages of memetic algorithm and particle swarm optimization and has been widely used in engineering fields. In order to overcome the shortcomings of local search in the classic SFLA, a novel update...
Particle Swarm Optimization has been widely used to solve real world problems, mainly when there are too many variables to be optimized and these variables are continuous. In nature one can observe many examples of cooperative behaviors that lead to complex problem solving. Recently, some Particle Swarm Optimization variations gracefully incorporate such cooperative features with consequent beneficial...
The paper develops a Multi-swarm particle swarm optimization (MPSO) to overcome the premature convergence problem. MPSO takes advantage of multiple sub-swarms with mixed search behavior to maintain the swarm diversity, and introduces cooperative mechanism to prompt the information exchange among sub-swarms. Moreover, MPSO adopts an adaptive reinitializing strategy guided by swarm diversity, which...
The particle swarm optimization (PSO), which goes right after Ant Colony Algorithm, is another new swarm intelligence algorithm. PSO has the same drawbacks as other optimization algorithms in spite of its predominance in some fields. That is easily falling into local optimization solution and low convergence velocity in the final stage. An improved algorithm called acceleration factors self-adaptive...
This paper proposes a new fuzzy tuned parameter particle swarm optimization (FPPSO) which remarkably outperforms the standard PSO as well as the previous fuzzy based approaches. Two benchmark functions with asymmetric initial range settings are used to validate the proposed algorithm and compare its performance with that of the other algorithms known as fuzzy based PSO. Numerical results indicate...
Particle swarm optimization (PSO) is a kind of random optimization algorithm based on the swarm intelligence. It has been used in many optimum problems and Its behave is better. This paper presents newly nonlinear self-adaptive parameters for the PSO (PSO-NL) and we compare it with the linear self-adaptive parameters for the PSO (PSO-TVAC). The experimental results show that the PSO-NL has a fast...
Particle swarm optimization (PSO) has been widely used to solve unconstrained optimization problems. However, problems in hyper dimensional spaces require the development of enhanced issues. For this, some variations of the original PSO form have been proposed, mainly concerning on the velocity update equation and sophisticated communication topologies of the swarm. In this paper, we propose a PSO...
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