On initial populations of a genetic algorithm for continuous optimization problems
Journal of Global Optimization
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Solving large scale optimization problems by opposition-based differential evolution (ODE)
WSEAS Transactions on Computers
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
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Nowadays, optimization problems with a few thousands of variables become more common. Population-based algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Evolutionary Strategies (ES) are commonly used approaches to solve complex large-scale problems from science and engineering. These approaches all work with a population of candidate solutions. On the other hand, for high-dimensional problems, no matter what is the individuals' distribution, the population is highly sparse. Therefore, intelligent employment of individual candidates can play a crucial role to find optimal solution(s) faster. The most majority of population-based algorithms utilize pseudo-random population initialization when there is no a priori knowledge about the solution. In this paper, a center-based population initialization is proposed and investigated on seven benchmark functions. The obtained results are compared with the results of Normal, Pseudo Random, and Latin Hypercube population initialization schemes. Furthermore, the advantages of the proposed center-based sampling method are investigated by a mathematical proof and also Monte Carlo (simulation) method. The detailed experimental verifications are provided for problems with 50, 500, and 1000 dimensions.