Improving the Bees Algorithm for Complex Optimisation Problems

摘要: An improved swarm-based optimisation algorithm from the Bees Algorithm family for solving complex problems is proposed. Like other Algorithms, performs a form of exploitative local search combined with random exploratory global search. This thesis details development and this demonstrates its robustness. The includes new method tuning called Meta functionality proposed compared to standard range state-of-the-art algorithms. A fitness evaluation has been developed enable solve stochastic problem. The modified was tested on parameter values Ant Colony Optimisation when Travelling Salesman Problems. Finally, adapted employed combinatorial problems. two neighbourhood operators such performance number travelling salesman problems, including printed circuit board assembly machine sequencing.