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This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of differential evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving...
Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequences, but are more useful for computational methods which depend on the generation of random numbers. Some of these tasks involve approximation of integrals in higher dimensions, simulation and global optimization. Sobol, Faure...
The multiobjective Quadratic Assignment Problem (mQAP) is considered as one of the hardest optimization problems but with many real-world applications. Since it may not be possible to simply weight the importance of each flow for the mQAP, it is best to use Pareto optimization to obtain the Pareto front or an approximation of it. Although Particle Swarm Optimization (PSO) algorithm has exhibited good...
Hardware/software partitioning is a crucial problem in embedded system design. In this paper, we provide an alternative approach to solve this problem using particle swarm optimization (PSO) algorithm. Performance analysis of the proposed scheme with integer linear programming, genetic algorithm and ant colony optimization technique has been compared using standard benchmark datasets, and the computer...
This paper introduces a hybrid metaheuristic, the variable neighborhood particle swarm optimization (VNPSO), consisting of a combination of the variable neighborhood search (VNS) and particle swarm optimization (PSO). The proposed VNPSO method is used for solving the multi-objective flexible job-shop scheduling problems (FJSP). The details of implementation for the multi-objective FJSP and the corresponding...
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