基于IEC的混合型隐性多目标决策方法研究
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摘要
在管理决策领域中,大量存在着一类混合型隐性多目标决策问题,此类决策问题具有以下三个显著特征:决策目标既包含有可以数量化表示的显性目标(Explicit Objectives),还包含难以数量化表示的隐性目标(Tacit Objectives);问题的可行解空间大,可行方案数目多(可以为无限多个);决策者偏好随着决策过程的进行可以调整改变;如人体工程学产品设计优化问题、工作间布局优化问题、旅游行程规划问题等。混合型隐性多目标决策问题是隐性目标决策领域的一类重要问题,研究适合处理混合型隐性多目标决策问题的决策方法是隐性目标决策的内在要求,具有重要的理论意义与应用价值。混合型隐性多目标决策问题具有的这些特征,使得该类决策问题求解异常复杂并且无法直接应用传统的多准则决策方法(如权重和法、效用函数法、妥协法等)加以求解,其求解过程需要采用具有交互机制的决策方式,以逐渐获取决策者的偏好。交互式进化计算(IEC,Interactive Evolutionary Computation)方法除了具有传统进化算法的优点外还具备与决策者进行交互的机制,兼具交互式系统的可适应性及本质上适合搜索复杂性问题的特性,使其成为适合处理混合型隐性多目标决策问题的主要方法。
本文从混合型隐性多目标决策问题的特征出发,分析了此类问题的求解难点,以IEC方法为技术基础,并结合多智能体计算技术与免疫计算技术,研究了混合型隐性多目标决策问题的智能决策方法,并建立了适合处理此类决策问题的优化决策支持模型。主要的研究内容包括:
(1)针对现有研究中缺乏混合型隐性多目标决策支持模型的问题,本文依据混合型隐性多目标决策问题的特征,从多目标优化决策的角度,将IEC方法的交互式决策机制与多目标进化算法(MOEAs,Multiobjective Optimization Evolutionary Algorithms)融合,提出了一种适应于混合型隐性多目标决策的优化决策支持模型,并讨论了在该模型框架下结合IEC与具有外部档案集的多目标进化算法的混合型隐性多目标决策问题求解过程,以说明该决策支持模型的可操作性。
(2)针对混合型隐性多目标决策的IEC决策方法中存在的进化效率问题,提出了一种高效的轮盘反转算子(RIO,Roulette Inversion Operator),并将之融入到基于IEC的混合型隐性多目标决策方法设计中,从算法的机理上来提高混合型隐性多目标决策的IEC方法的进化效率。理论分析证明了轮盘反转算子能够有效克服John Holland遗传算法理论中提出的反转算子(HIO,Holland Inversion Operator)在实数编码算法应用中的固有不合理性;数值实验也验证了RIO算子的优越性。进而根据混合型隐性多目标决策问题的特征,结合RIO算子与多智能体计算技术,通过定义智能体、智能体生存环境及智能体在环境中的行为规则,如扩散、变异、竞争死亡、再生、自学习等智能体行为规则,提出了一种交互式多智能体多目标进化求解算法,该算法充分利用了人的智能和多智能体计算技术的特点,使得用户每次只需选择最好的与最差的个体,用户不需对个体给出具体的适应值,有效缩短了用户对每一代种群的评价时间,从而减轻用户评价疲劳。工作间布局优化仿真实验验证了该方法的有效性,且能够有效缓解用户疲劳。
(3)针对混合型隐性多目标决策过程中个体多样性缺失问题,研究了基于种群熵信息保持种群多样性的策略。将种群熵抽样方法与一种自适应变异算子相结合提出了一种基于种群熵信息的自适应种群多样性保持策略,并基于该策略提出了一种小种群遗传算法,数值实验表明该策略能够在小种群规模下使得算法有效保持种群多样性,预防算法陷入局部寻优,提高算法性能,适合在IEC中应用;进而将此多样性保持策略融于混合型隐性多目标决策问题的交互式求解算法设计中,提出了一种交互式多目标进化求解算法,工作间布局优化仿真实验验证了算法的有效性。
(4)针对混合型隐性多目标决策问题求解效率低的问题,将免疫计算技术引入到交互式进化计算领域中来研究新颖高效的智能决策方法,以支持混合型隐性多目标决策问题求解。提出了一种交互式免疫多目标进化求解算法;并将免疫单/多克隆策略与多智能体计算技术结合,定义了免疫智能体、免疫智能体生存环境以及免疫智能体的免疫行为规则,如抗体多克隆、抗体单克隆、抗体死亡、抗体再生与抗体自学习等行为规则,提出了一种交互式免疫智能体多目标进化求解算法。在两种算法中每次评价只需要决策者选出最好与最坏的个体,这样的评价策略使得评价过程轻松快捷,能够有效减轻用户评价疲劳。通过服装选购推荐问题仿真实验可以看出,两种算法都优于传统的序列交互式进化算法,且能够有效缓解用户疲劳。
(5)设计了一种支持混合型隐性多目标决策问题求解的智能决策支持原型系统,并对系统中各模块的功能进行了分析讨论。研究了“混合型隐性多目标决策问题”的一个具体实例—服装选购推荐问题,分析了服装商品的编码及其求解思路,研究了基于IEC的服装选购推荐系统的基本流程,给出了系统功能的具体实现。
混合型隐性多目标决策问题是大量存在于管理决策领域中的一类复杂决策问题,本文从多目标优化决策角度,建立了适合处理混合型隐性多目标决策问题的优化决策支持模型,围绕混合型隐性多目标决策问题提出了一些新颖有效的基于IEC的智能交互式多目标决策方法,以期更好的支持混合型隐性多目标决策问题的求解。这些研究成果丰富了该领域的研究内容,能够为实际混合型隐性多目标决策问题的求解提供方法指导和技术支持。
In the management and decision-making field, there exit many Hybrid Multi-objective Decision-making Problems with Tacit Objectives (HMDMPTO) which are important problems in the research area of Tacit Objective Decision-Making Problems (TODMP). The HMDMPTO problems have three marked features: the objectives in HMDMPTO problems are consisted of some objectives which are hard to be defined explicitly and other objectives which are able to be defined explicitly, the problems also have a very large number of decision solutions, even infinitely many; the preference structure of decision-maker is of uncertainty, and it needs to be confirmed gradually in the interactive process of problem solving. For examples, the ergonomic chair design problem, the manufacturing plant layout problem and the travel itinerary planning problem. It is the intrinsical requirement of the TODMP research to develop the appropriate method for the HMDMPTO problems, which is of great theoretical significance and application value. The marked features of the HMDMPTO problems make it hard to very complex to solve the kind of decision-making problems and unable to apply the traditional method such as weighted sum methods, utility function methods and compromise methods etc. The problem-solving process of the HMDMPTO problems requires interactive mechanism so that the preference structure can be obtained and conceived gradually. In recent years, with the development of intelligent optimization algorithms and artificial intelligence technologies, the Interactive Evolutionary Computation (IEC) methods with human-computer interaction mechanisms has become a strong advantageous method suitable for solving the HMDMPTO problems, especially its interactive mechanism and merits for solving complex problems inherited from the Evolutionary Computation methods.
This dissertation analyses the difficulties of solving the HMDMPTO problems from the angle of introducing the features of the HMDMPTO problems, then studies some intelligent decision-making methods of the HMDMPTO problems on the basis of IEC and the decision-making supportive model for the HMDMPTO problems, in combination with multi-agent intelligent and artificial immune computation techniques. The main achievements of this dissertation are as follows:
(1) In the view of the lack of decision-making supportive model for the HMDMPTO problems in current research, in this dissertation a novel decision-making supportive model for the HMDMPTO problems is proposed by melting the interactive decision-making mechanism of IEC and the Multiobjective Optimization Evolutionary Algorithms (MOEAs), and the concrete problem-solving process of the instantial method constructed by melting the IEC and the Archived MOEA method under the structure of the proposed model to illustrate the operability of the model.
(2) In the view of evolutionary efficiency problem of the IEC-based method for the HMDMPTO problems, this dissertation proposed a novel Roulette Inversion Operator (RIO) of higher stochastic search ability, and the RIO operator is integrated into the design process of the methods for the HMDMPTO problems in order to improve the evolutionary efficiency from the angle of the algorithm mechanism. Theoretical analysis proves the RIO operator can conquer the intrinsical shortcoming of John Holland's Inversion Operator (HIO) when used for real coded stochastic algorithms. The experiments of several benchmark functions also show the superiority of the RIO operator. Then according to the features of the HMDMPTO problems, an interactive multi-agent multi-objective optimization evolutionary algorithm (IMAMOEA) is developed by melting RIO operator with the multi-agent computation technique to define the Agents, Living Environment for Agents and behavior rules of the Agents such as dispersion, mutation, competitive death, rebirth and self-learning. The IMAMOEA takes good use of human intelligence and the characters of agent-based computation technique to let the users only need to select the best and the worst individuals of the candidate population which can effectively shorten the evaluation time and therefore reduce the user's evaluation burden. The experiment for the manufacturing plant layout problem shows the validity of the proposed IMAMOEA algorithm.
(3) The diversity-missing problem of feasible solution population in the IEC-based decision-making process of the HMDMPTO problems. In this dissertation, an adaptive strategy for maintaining population diversity is designed by integrating the population entropy sampling method and a adaptive mutation operator. In order to examine the validity of the designed strategy, a small population genetic algorithm integrating the diversity-maintaining strategy is developed, and the numerical experiments show the good performance of the genetic algorithm and validity of the strategy. Furthermore, an interactive multi-objective evolutionary algorithm (IAMEA) with the application of the diversity maintaining strategy is proposed. The experiment for the manufacturing plant layout problem shows the validity of the proposed IAMEA algorithm.
(4) In the view of evolutionary efficiency problem of the IEC-based method for the HMDMPTO problems, in this dissertation, an interactive immune multi-objective evolutionary algorithm (IMMEA) is designed by introducing the artificial immune computation technique into the IEC research area, furthermore, another immune-based interactive multi-agent multi-objective evolutionary algorithm (IIMOEA) is proposed by fusing the immune cloning strategy into the multi-agent computation technique to define the immune Agents (antibodies), Living Environment for the immune Agents and the behavior rules for the immune Agents such as antibody polyclonal behavior, antibody monoclonal behavior, antibody death behavior, antibody rebirth behavior and antibody self-learning behavior to support the decision-making of the HMDMPTO problems. In both IMMEA and IIMOEA, decision-makers are asked only to select the best and worst solutions of the current candidate individuals which makes the evaluation process easy and therefore reduces the decision-makers' evaluation burden. The experiments for the apparel-purchasing recommendation problem show that both the IMMEA and IIMOEA are valid and better than the traditional sequential interactive genetic algorithm.
(5) A prototype of decision support system for the HMDMPTO problems is studied, and its modules are discussed. And the apparel-purchasing recommendation problem as an instance of the HMDMPTO problems is studied. The coding and solving ideal of the apparel-purchasing recommendation problem is analyzed and discussed. The implement flow of apparel-purchasing recommendation system based on IEC is studied. The functions of the developed system are discussed.
The HMDMPTO problems are a class of complex decision-making problems, and these problems are pervasive in our real life. From the perspective of multi-objective decision-making, this dissertation firstly constructed a novel decision-making supportive model for the HMDMPTO problems, then this dissertation studies and proposes several new intelligent IEC-based decision-making optimization methods for the HMDMPTO problems in order to improve the HMDMPTO's decision-making efficiency. The results of these studies enrich the contents in the TODMP research field, and can provide methodological guidance and technical support for the actual decision-making.
本文从混合型隐性多目标决策问题的特征出发,分析了此类问题的求解难点,以IEC方法为技术基础,并结合多智能体计算技术与免疫计算技术,研究了混合型隐性多目标决策问题的智能决策方法,并建立了适合处理此类决策问题的优化决策支持模型。主要的研究内容包括:
(1)针对现有研究中缺乏混合型隐性多目标决策支持模型的问题,本文依据混合型隐性多目标决策问题的特征,从多目标优化决策的角度,将IEC方法的交互式决策机制与多目标进化算法(MOEAs,Multiobjective Optimization Evolutionary Algorithms)融合,提出了一种适应于混合型隐性多目标决策的优化决策支持模型,并讨论了在该模型框架下结合IEC与具有外部档案集的多目标进化算法的混合型隐性多目标决策问题求解过程,以说明该决策支持模型的可操作性。
(2)针对混合型隐性多目标决策的IEC决策方法中存在的进化效率问题,提出了一种高效的轮盘反转算子(RIO,Roulette Inversion Operator),并将之融入到基于IEC的混合型隐性多目标决策方法设计中,从算法的机理上来提高混合型隐性多目标决策的IEC方法的进化效率。理论分析证明了轮盘反转算子能够有效克服John Holland遗传算法理论中提出的反转算子(HIO,Holland Inversion Operator)在实数编码算法应用中的固有不合理性;数值实验也验证了RIO算子的优越性。进而根据混合型隐性多目标决策问题的特征,结合RIO算子与多智能体计算技术,通过定义智能体、智能体生存环境及智能体在环境中的行为规则,如扩散、变异、竞争死亡、再生、自学习等智能体行为规则,提出了一种交互式多智能体多目标进化求解算法,该算法充分利用了人的智能和多智能体计算技术的特点,使得用户每次只需选择最好的与最差的个体,用户不需对个体给出具体的适应值,有效缩短了用户对每一代种群的评价时间,从而减轻用户评价疲劳。工作间布局优化仿真实验验证了该方法的有效性,且能够有效缓解用户疲劳。
(3)针对混合型隐性多目标决策过程中个体多样性缺失问题,研究了基于种群熵信息保持种群多样性的策略。将种群熵抽样方法与一种自适应变异算子相结合提出了一种基于种群熵信息的自适应种群多样性保持策略,并基于该策略提出了一种小种群遗传算法,数值实验表明该策略能够在小种群规模下使得算法有效保持种群多样性,预防算法陷入局部寻优,提高算法性能,适合在IEC中应用;进而将此多样性保持策略融于混合型隐性多目标决策问题的交互式求解算法设计中,提出了一种交互式多目标进化求解算法,工作间布局优化仿真实验验证了算法的有效性。
(4)针对混合型隐性多目标决策问题求解效率低的问题,将免疫计算技术引入到交互式进化计算领域中来研究新颖高效的智能决策方法,以支持混合型隐性多目标决策问题求解。提出了一种交互式免疫多目标进化求解算法;并将免疫单/多克隆策略与多智能体计算技术结合,定义了免疫智能体、免疫智能体生存环境以及免疫智能体的免疫行为规则,如抗体多克隆、抗体单克隆、抗体死亡、抗体再生与抗体自学习等行为规则,提出了一种交互式免疫智能体多目标进化求解算法。在两种算法中每次评价只需要决策者选出最好与最坏的个体,这样的评价策略使得评价过程轻松快捷,能够有效减轻用户评价疲劳。通过服装选购推荐问题仿真实验可以看出,两种算法都优于传统的序列交互式进化算法,且能够有效缓解用户疲劳。
(5)设计了一种支持混合型隐性多目标决策问题求解的智能决策支持原型系统,并对系统中各模块的功能进行了分析讨论。研究了“混合型隐性多目标决策问题”的一个具体实例—服装选购推荐问题,分析了服装商品的编码及其求解思路,研究了基于IEC的服装选购推荐系统的基本流程,给出了系统功能的具体实现。
混合型隐性多目标决策问题是大量存在于管理决策领域中的一类复杂决策问题,本文从多目标优化决策角度,建立了适合处理混合型隐性多目标决策问题的优化决策支持模型,围绕混合型隐性多目标决策问题提出了一些新颖有效的基于IEC的智能交互式多目标决策方法,以期更好的支持混合型隐性多目标决策问题的求解。这些研究成果丰富了该领域的研究内容,能够为实际混合型隐性多目标决策问题的求解提供方法指导和技术支持。
In the management and decision-making field, there exit many Hybrid Multi-objective Decision-making Problems with Tacit Objectives (HMDMPTO) which are important problems in the research area of Tacit Objective Decision-Making Problems (TODMP). The HMDMPTO problems have three marked features: the objectives in HMDMPTO problems are consisted of some objectives which are hard to be defined explicitly and other objectives which are able to be defined explicitly, the problems also have a very large number of decision solutions, even infinitely many; the preference structure of decision-maker is of uncertainty, and it needs to be confirmed gradually in the interactive process of problem solving. For examples, the ergonomic chair design problem, the manufacturing plant layout problem and the travel itinerary planning problem. It is the intrinsical requirement of the TODMP research to develop the appropriate method for the HMDMPTO problems, which is of great theoretical significance and application value. The marked features of the HMDMPTO problems make it hard to very complex to solve the kind of decision-making problems and unable to apply the traditional method such as weighted sum methods, utility function methods and compromise methods etc. The problem-solving process of the HMDMPTO problems requires interactive mechanism so that the preference structure can be obtained and conceived gradually. In recent years, with the development of intelligent optimization algorithms and artificial intelligence technologies, the Interactive Evolutionary Computation (IEC) methods with human-computer interaction mechanisms has become a strong advantageous method suitable for solving the HMDMPTO problems, especially its interactive mechanism and merits for solving complex problems inherited from the Evolutionary Computation methods.
This dissertation analyses the difficulties of solving the HMDMPTO problems from the angle of introducing the features of the HMDMPTO problems, then studies some intelligent decision-making methods of the HMDMPTO problems on the basis of IEC and the decision-making supportive model for the HMDMPTO problems, in combination with multi-agent intelligent and artificial immune computation techniques. The main achievements of this dissertation are as follows:
(1) In the view of the lack of decision-making supportive model for the HMDMPTO problems in current research, in this dissertation a novel decision-making supportive model for the HMDMPTO problems is proposed by melting the interactive decision-making mechanism of IEC and the Multiobjective Optimization Evolutionary Algorithms (MOEAs), and the concrete problem-solving process of the instantial method constructed by melting the IEC and the Archived MOEA method under the structure of the proposed model to illustrate the operability of the model.
(2) In the view of evolutionary efficiency problem of the IEC-based method for the HMDMPTO problems, this dissertation proposed a novel Roulette Inversion Operator (RIO) of higher stochastic search ability, and the RIO operator is integrated into the design process of the methods for the HMDMPTO problems in order to improve the evolutionary efficiency from the angle of the algorithm mechanism. Theoretical analysis proves the RIO operator can conquer the intrinsical shortcoming of John Holland's Inversion Operator (HIO) when used for real coded stochastic algorithms. The experiments of several benchmark functions also show the superiority of the RIO operator. Then according to the features of the HMDMPTO problems, an interactive multi-agent multi-objective optimization evolutionary algorithm (IMAMOEA) is developed by melting RIO operator with the multi-agent computation technique to define the Agents, Living Environment for Agents and behavior rules of the Agents such as dispersion, mutation, competitive death, rebirth and self-learning. The IMAMOEA takes good use of human intelligence and the characters of agent-based computation technique to let the users only need to select the best and the worst individuals of the candidate population which can effectively shorten the evaluation time and therefore reduce the user's evaluation burden. The experiment for the manufacturing plant layout problem shows the validity of the proposed IMAMOEA algorithm.
(3) The diversity-missing problem of feasible solution population in the IEC-based decision-making process of the HMDMPTO problems. In this dissertation, an adaptive strategy for maintaining population diversity is designed by integrating the population entropy sampling method and a adaptive mutation operator. In order to examine the validity of the designed strategy, a small population genetic algorithm integrating the diversity-maintaining strategy is developed, and the numerical experiments show the good performance of the genetic algorithm and validity of the strategy. Furthermore, an interactive multi-objective evolutionary algorithm (IAMEA) with the application of the diversity maintaining strategy is proposed. The experiment for the manufacturing plant layout problem shows the validity of the proposed IAMEA algorithm.
(4) In the view of evolutionary efficiency problem of the IEC-based method for the HMDMPTO problems, in this dissertation, an interactive immune multi-objective evolutionary algorithm (IMMEA) is designed by introducing the artificial immune computation technique into the IEC research area, furthermore, another immune-based interactive multi-agent multi-objective evolutionary algorithm (IIMOEA) is proposed by fusing the immune cloning strategy into the multi-agent computation technique to define the immune Agents (antibodies), Living Environment for the immune Agents and the behavior rules for the immune Agents such as antibody polyclonal behavior, antibody monoclonal behavior, antibody death behavior, antibody rebirth behavior and antibody self-learning behavior to support the decision-making of the HMDMPTO problems. In both IMMEA and IIMOEA, decision-makers are asked only to select the best and worst solutions of the current candidate individuals which makes the evaluation process easy and therefore reduces the decision-makers' evaluation burden. The experiments for the apparel-purchasing recommendation problem show that both the IMMEA and IIMOEA are valid and better than the traditional sequential interactive genetic algorithm.
(5) A prototype of decision support system for the HMDMPTO problems is studied, and its modules are discussed. And the apparel-purchasing recommendation problem as an instance of the HMDMPTO problems is studied. The coding and solving ideal of the apparel-purchasing recommendation problem is analyzed and discussed. The implement flow of apparel-purchasing recommendation system based on IEC is studied. The functions of the developed system are discussed.
The HMDMPTO problems are a class of complex decision-making problems, and these problems are pervasive in our real life. From the perspective of multi-objective decision-making, this dissertation firstly constructed a novel decision-making supportive model for the HMDMPTO problems, then this dissertation studies and proposes several new intelligent IEC-based decision-making optimization methods for the HMDMPTO problems in order to improve the HMDMPTO's decision-making efficiency. The results of these studies enrich the contents in the TODMP research field, and can provide methodological guidance and technical support for the actual decision-making.
引文
[1].Brintrup A.M.,Ramsden Jeremy,Takagi Hideyuki et al.Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms[J].IEEE Trans on Evolutionary Computation,2008,12(3):343-354.
[2].Brintrup Alexandra Melike,Ramsden Jeremy,Tiwari Ashutosh.An interactive genetic algorithm-based framework for handling qualitative criteia in design optimization[J].Computers in Industry,2007,58(3):279-291.
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