CAJ | 학술논문

群体智能算法模拟生物进化或动物群体协作的搜索机制，其目标是快速有效地搜索复杂优化问题的解空间，寻求全局最优解．本文通过对群体智能算法的搜索机理进行分析，根据在搜索过程中解集内部结构变化的性质定义了解集多样度，并在此基础上研究了两种基本的搜索策略——多样化搜索和集中化搜索对解集进化过程中的停滞性的影响，证明了集中化搜索不可避免地使解集中的候选解逐渐趋于单一，是导致算法停滞收敛的主要原因；而多样化搜索能从任何候选解出发搜索到整个编码空间中的任一个点，即整个空间是多样化搜索的可达域，但将使算法不收敛．本文采用三类典型的群体智能算法：遗传算法、蚁群算法和粒子群算法进行了实验，验证了上述分析结论的正确性．
군체지능산법모의생물진화혹동물군체협작적수색궤제，기목표시쾌속유효지수색복잡우화문제적해공간，심구전국최우해．본문통과대군체지능산법적수색궤리진행분석，근거재수색과정중해집내부결구변화적성질정의료해집다양도，병재차기출상연구료량충기본적수색책략——다양화수색화집중화수색대해집진화과정중적정체성적영향，증명료집중화수색불가피면지사해집중적후선해축점추우단일，시도치산법정체수렴적주요원인；이다양화수색능종임하후선해출발수색도정개편마공간중적임일개점，즉정개공간시다양화수색적가체역，단장사산법불수렴．본문채용삼류전형적군체지능산법：유전산법、의군산법화입자군산법진행료실험，험증료상술분석결론적정학성．
Swarm intelligent algorithms are derived from the simulation of natural evolution or collective behavior of animals to seek solutions of complicated optimization problems by exploring and exploiting the search space efficiently and effectively. Through the analysis on the search characteristics of swarm intelligent algorithms, the concept of solution set diversity is introduced in this paper according to the infrastructural changes of solution sets in search processes. Two categories of fundamental search strategies, i.e. the diversification search and the intensification search, are then defined and their influences on the stagnation of solution sets evolution are investigated on the basis of the solution set diversity. It is proved in this paper that an intensification strategy inevitably leads candidates solution to a single solution, which is one of the main sources of stagnation; while a diversification strategy is able to reach any point of the coding space from each initial candidate solution, i.e., the whole searching space is the reachable region of the diversification strategy, but the convergence of the algorithm cannot be guaranteed. Three popular swarm intelligent algorithms, i.e. the canonical generic algorithm, the ant colony system and the discrete particle swarm optimization, are tested with a benchmark problem, and the results support the theoretical conclusions.