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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)
— 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 Quasi-Oppositional DE(More)
— In this paper, a time varying jumping rate (TVJR) model for Opposition-Based Differential Evolution (ODE) has been proposed. According to this model, the jumping rate changes linearly during the evolution based on the number of function evaluations. A test suite with 15 well-known benchmark functions has been employed to compare performance of the DE and(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)
This work investigates the performance of Differential Evolution (DE) and its opposition-based version (ODE) on large scale optimization problems. Opposition-based differential evolution (ODE) has been proposed based on DE; it employs opposition-based population initialization and generation jumping to accelerate convergence speed. ODE shows promising(More)