Abstract
Nonlinear Complex optimization problems are the key area of research in the field of optimization. Evolutionary Algorithms (EAs) are applied to solve these optimization problems successfully. The EAs suffer a lot due to their slow convergence rate, primarily due to evolutionary nature of these algorithms. It has been proved that distribution of initial population into the search space effects the evolutionary algorithm performance. This paper presents a novel initialization method for genetic algorithms, in which opposite of the population is created. The best individuals from the population and its opposite are selected as the initial population. This provides a better starting point for search through the solution space. To increase the convergence speed of EAs, a probabilistic excitation scheme for chromosomes is also introduced. This scheme tunes the population effectively during the evolutionary process. The performance of the algorithm is tested over suit of 10 functions with different dimensions. Opposition based Differential Evolution and Genetic Algorithms are used as competitor algorithms to compare the results of the proposed algorithm. Various sets of experiments are performed. The results show that the proposed method outperforms Opposition based Differential Evolution and Genetic algorithms for most of the test functions.