Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many domains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings. In fact, finding appr...Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many domains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings. In fact, finding appropriate settings of the algorithm’s parameters is considered to be a nontrivial task and is usually set manually to values that are known to give reasonable performance. In this paper, Ant Colony Optimization with Parametric Analysis (ACO-PA) is developed to overcome this drawback. The main feature of the ACO-PA is the ability of deciding the appropriate parameter values within the predefined parameter variations. Besides, a new approach which enables the pheromone information value to be proportional to the heuristic information value is introduced. The effectiveness of the proposed algorithm is investigated through the application of the algorithm to the construction site layout problems taken from the state-of-art. Results show that the ACO-PA can reduce transportation cost up to 16.8% compared to the site layouts generated by Genetic Algorithms and basic ACO. Moreover, the effects of parameter settings on the generated solutions are investigated.展开更多
为提高山区隧道施工场地布置方案决策的准确性,以牛栾村隧道六种施工场地布置方案为例,提出了基于改进灰靶的方案优选模型。首先,通过分析场地布置方案影响因素,构建了以方案可行性、方案经济性、环境影响和社会效益影响为核心的评价指...为提高山区隧道施工场地布置方案决策的准确性,以牛栾村隧道六种施工场地布置方案为例,提出了基于改进灰靶的方案优选模型。首先,通过分析场地布置方案影响因素,构建了以方案可行性、方案经济性、环境影响和社会效益影响为核心的评价指标体系;其次,采用云模型将定性指标定量描述,并运用CRITIC(criteria importance though intercriteria correlation)确定指标权重;最后以灰色关联差异信息值为基础,结合欧几里得理论计算修正的加权靶心距,通过对比靶心距实现方案优选,并采用单因素轮换法(one-at-a-time,OAT)进行了指标敏感性分析。结果表明:最优方案的加权靶心距为0.610,评选出的方案与实际一致,并分析出“地形地貌改变”为对方案评选影响最大的指标。可见,该方法呈现了各方案的优劣,使山区隧道施工场地布置方案评选更科学、合理。展开更多
文摘Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many domains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings. In fact, finding appropriate settings of the algorithm’s parameters is considered to be a nontrivial task and is usually set manually to values that are known to give reasonable performance. In this paper, Ant Colony Optimization with Parametric Analysis (ACO-PA) is developed to overcome this drawback. The main feature of the ACO-PA is the ability of deciding the appropriate parameter values within the predefined parameter variations. Besides, a new approach which enables the pheromone information value to be proportional to the heuristic information value is introduced. The effectiveness of the proposed algorithm is investigated through the application of the algorithm to the construction site layout problems taken from the state-of-art. Results show that the ACO-PA can reduce transportation cost up to 16.8% compared to the site layouts generated by Genetic Algorithms and basic ACO. Moreover, the effects of parameter settings on the generated solutions are investigated.
文摘为提高山区隧道施工场地布置方案决策的准确性,以牛栾村隧道六种施工场地布置方案为例,提出了基于改进灰靶的方案优选模型。首先,通过分析场地布置方案影响因素,构建了以方案可行性、方案经济性、环境影响和社会效益影响为核心的评价指标体系;其次,采用云模型将定性指标定量描述,并运用CRITIC(criteria importance though intercriteria correlation)确定指标权重;最后以灰色关联差异信息值为基础,结合欧几里得理论计算修正的加权靶心距,通过对比靶心距实现方案优选,并采用单因素轮换法(one-at-a-time,OAT)进行了指标敏感性分析。结果表明:最优方案的加权靶心距为0.610,评选出的方案与实际一致,并分析出“地形地貌改变”为对方案评选影响最大的指标。可见,该方法呈现了各方案的优劣,使山区隧道施工场地布置方案评选更科学、合理。
