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The course timetabling problem (CTP), as a typical combinatorial optimization problem (COP), has been proved to be NP-Complete. Due to its complexity, the general genetic algorithm converges slowly and easily converges to local optima. A novel quantum-inspired evolutionary algorithms (QEA) is put forward for the CTP. The QEA uses genetic operators of Q-bit as well as updating operator of quantum gate...
Particle filters perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. Unfortunately, there are some cases in which most particles are concentrated prematurely at a wrong point, thereby losing diversity and causing the estimation to fail. In this paper, genetic algorithms (GAs) are incorporated into a particle filter to overcome this drawback...
This paper proposes an algorithm to solve multi-objective problems by Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) which uses an external population, called pareto vector set P, in genetic operators. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isn't so efficient. To increase its...
Premature convergence in the process of genetic algorithm (GA) for searching solution is frequently faced and the evolutionary processes are often trapped in a local but not global optimum. This phenomenon occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature,...
In this paper, the authors propose two new evolutionary approaches to Multiobjective Optimization Problems (MOPs)-Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA). In DPSMO, instead of using genetic operators (e.g., crossover and mutation), the information sharing technique in Partide Swarm Optimization (PSO) is applied to inform the entire population...
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