CAJ | 학술논문

针对传统的量子进化算法只使用当前最优个体作为指导，存在进化能力不足，易陷入局部极值的问题，提出了一种结合远离最差策略的自适应量子进化算法KSQEA，使个体在进化过程中不仅向最优个体靠近，而且还远离最差个体，这样在最优个体优势不明显时仍有可能获得进化动力。旋转角更新则采用一种新的自适应波浪式衰减方式，以更好地平衡探查和利用。在函数优化和背包问题上的实验结果表明，以上措施有效地增强了算法的搜索能力，提高了解的质量。
침대전통적양자진화산법지사용당전최우개체작위지도，존재진화능력불족，역함입국부겁치적문제，제출료일충결합원리최차책략적자괄응양자진화산법KSQEA，사개체재진화과정중불부향최우개체고근，이차환원리최차개체，저양재최우개체우세불명현시잉유가능획득진화동력。선전각경신칙채용일충신적자괄응파랑식쇠감방식，이경호지평형탐사화이용。재함수우화화배포문제상적실험결과표명，이상조시유효지증강료산법적수색능력，제고료해적질량。
In order to overcome the limit that the traditional quantum-inspired evolutionary algorithms (QEA) only use the current best individuals to guide the evolution, which leads to the insufficient evolutionary capability and may often end up by providing sub-optimal solutions, this paper proposes a self-adaptive QEA combining the strategy of keeping away from the worst (KSQEA), which evolves individuals not only close to the best but also far away from the worst. In this way, KSQEA is able to acquire the evolutionary driving force even if the advantage of the best individuals is not obvious. In addition, this paper proposes a wavy rotation angle decaying method to balance the exploration and the exploitation of search. The experimental results both on the function optimization and the knapsack problem show that these measures successfully increase the searching capability of the algorithm and the quality of solutions.