A quantum inspired competitive coevolution evolutionary algorithm

Abstract

Continued and rapid improvement in evolutionary algorithms has made them suitable technologies for tackling many difficult optimization problems. Recently the introduction of quantum inspired evolutionary computation has opened a new direction for further enhancing the effectiveness of these algorithms. Existing studies on quantum inspired algorithms focused primarily on evolving a single set of homogeneous solutions. This thesis expands the scope of current research by applying quantum computing principles, in particular the quantum superposition principle, to competitive coevolution algorithms (CCEA) and proposes a novel Quantum inspired Competitive Coevolutionary Algorithm (QCCEA). QCCEA uses a new approach to quantize candidate solution unlike previous quantum evolutionary algorithms that use qubit representation. The proposed QCCEA quantifies the selection procedure using normal distribution, which empowers the algorithm to reach the optimal fitness faster than original CCEA. QCCEA is evaluated against CCEA on twenty benchmark numerical optimization problems. The experimental results show that QCCEA performed significantly better than CCEA for most benchmark functions.