Particle Swarm Optimization (PSO) is a population based optimal method and very simple in both theory and numerical implementation. Nowadays, PSO has been recognized as a paradigm for numerical optimizations; however, PSO is easily trapped into a local optimum when solving multidimensional and complex problems. In order to overcome this difficulty, this paper presents a modified PSO with a dyna...

The double-controlled metric-type space (X,D) is a metric in which the triangle inequality has form D(η,μ)≤ζ1(η,θ)D(η,θ)+ζ2(θ,μ)D(θ,μ) for all η,θ,μ∈X. maps ζ1,ζ2:X×X→[1,∞) are called control functions. In this paper, we introduce novel generalization of double-composed space, where D(η,μ)≤αD(η,θ)+βD(θ,μ) our new functions α,β:[0,∞)→[0,∞) composed D inequality, multiplied with D. We establish s...

In order to effectively solve combinatorial optimization problems, the Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimization (PSO) combine to form a new ED-PSO hybrid algorithm, the algorithm can effectively apply global statistical information and global optimal solution to the solution space search. This algorithm is used to solve the Multidimensional Knapsack Problem (MK...

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can b...

so far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is particle swarm optimization (pso). prior some efforts by applying fuzzy logic for improving defects of pso such as trapping in local optimums and early convergence has been done. moreover to overcome the problem of i...

The paper presents a novel particle swarm optimizer (PSO), called gender-hierarchy particle swarm optimizer based on punishment (GH-PSO). In the proposed algorithm, the social part and recognition part of PSO both are modified in order to accelerate the convergence and improve the accuracy of the optimal solution. Especially, a novel recognition approach, called general recognition, is presente...

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

Particle Swarm Optimization is currently employed in several optimization and search problems due its ease and ability to find solutions successfully. A variant of PSO, called as Improved PSO has been developed in this paper and is hybridized with the simulated annealing approach to achieve better solutions. The hybrid technique has been employed, inorder to improve the performance of improved ...

This paper addresses the problem of an efficient predictive density estimation for q(‖y−θ‖2) Y based on X∼p(‖x−θ‖2) y,x,θ∈Rd. The chosen criteria are integrated L1 loss given by L(θ,qˆ)=∫Rd|qˆ(y)−q(‖y−θ‖2)|dy, and associated frequentist risk, θ∈Θ. For absolutely continuous strictly decreasing q, we establish inevitability scale expansion improvements qˆc(y;X)=1cdq(‖y−X‖2/c2) over plug-in qˆ1, a...

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. Being a stochastic algorithm, PSO and its randomness present formidable challenge for the theoretical analysis of it, and few of the existing PSO improvements have make an effort to eliminate the random coefficients in the PSO updating formula. This paper analyzes the importance of the randomness in the...