Shahryar Rahnamayan

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Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper presents a novel algorithm to accelerate the differential evolution (DE). The proposed opposition-based DE(More)
Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel(More)
Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this(More)
a School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China b School of Computer, China University of Geosciences, Wuhan 430072, PR China c Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, Canada L1H 7K4 d Shenzhen Graduate School,(More)
In this paper, an enhanced version of the Opposition-Based Differential Evolution (ODE) is proposed. ODE utilizes opposite numbers in the population initialization and generation jumping to accelerate Differential Evolution (DE). Instead of opposite numbers, in this work, quasi opposite points are used. So, we call the new extension QuasiOppositional DE(More)
Differential evolution (DE) is a well-known algorithm for global optimization over continuous search spaces. However, choosing the optimal control parameters is a challenging task because they are problem oriented. In order to minimize the effects of the control parameters, a Gaussian bare-bones DE (GBDE) and its modified version (MGBDE) are proposed which(More)