A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation

Recent research on Distributed Artificial Intelligence(DAI)has focused upon agents‘ interaction in Multiagent System.sThis paper presents a text understanding oriented multiagent dynamic interaction testbed(TUMIT);the theoretic framework based upon game theory,the free-market-like system marchitecture,and experimentation on TUMIT.Unlike other DAI testbeds,TUMIT views different text understanding(TU)methods as different“computational resources”,and makes agents choose different TU paths and computational resources according to the resouce information on the bulletins in their hostcomputer.Therefore,in TUMIT,task allocation is wholly distributed.This makes TUMIT work like a “free market”.In such a system,agents‘choices and resource load may oscillate.It is shown theoretically and experimentally that if agents use multi-level of “history information”,their behavior will tend to converge to a Nash equilibrium situation;and that if agents use “fecall-forget” strategy on “history information”,the convergence can be accelerated and the agents can acclimate themselves to changed environment.Compared with other DAI testbeds,TUMIT is more distributed,and the agents in TUMIT are more adaptive to the dynamic environment.     

A multiagent architecture for fuzzy modeling

In this paper a hybrid learning system that combines different fuzzy modeling techniques is being investigated. In order to implement the different methods, we propose the use of intelligent agents, which collaborate by means of a multiagent architecture. This approach, involving agents which embody the different problem solving methods, is a potentially useful strategy for enhancing the power of fuzzy modeling systems. ©1999 John Wiley & Sons, Inc.     

Immune clonal coevolutionary algorithm for dynamic multiobjective optimization

In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.     

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.     

Evolutionary algorithms with dynamic population size and localexploration for multiobjective optimization

Evolutionary algorithms have been recognized to be well suited for multiobjective optimization. These methods, however, need to "guess" for an optimal constant population size in order to discover the usually sophisticated tradeoff surface. This paper addresses the issue by presenting a novel incrementing multiobjective evolutionary algorithm (IMOEA) with dynamic population size that is computed adaptively according to the online discovered tradeoff surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine tuning for broader neighborhood exploration. This achieves better convergence as well as discovering any gaps or missing tradeoff regions at each generation. Other advanced features include a proposed preserved strategy to ensure better stability and diversity of the Pareto front and a convergence representation based on the concept of online population domination to provide useful information. Extensive simulations are performed on two benchmark and one practical engineering design problems     

动态多目标优化的预测遗传算法

为了在动态环境中很好地跟踪最优解,考虑动态优化问题的特点,提出一种新的多目标预测遗传算法.首先对 Pareto 前沿面进行聚类以求得解集的质心;其次应用该质心与参考点描述 Pareto 前沿面;再次通过预测方法给出预测点集,使得算法在环境变化后能够有指导地增加种群多样性,以便快速跟踪最优解;最后应用标准动态测试问题进行算法测试,仿真分析结果表明所提出算法能适应动态环境,快速跟踪 Pareto 前沿面.     

多目标动态优化中Pareto随机合作博弈研究综述

随着经济全球化的不断深入,“合作共赢”的发展战略越来越被人们接受,进而合作博弈也被合理地应用到多个领域.与静态合作博弈相比,动态博弈的约束条件为动态方程,其具有优化行为、多个玩家共同存在、决策结果的持久性以及对环境变化的鲁棒性等特点.由于动态系统总是受到某些随机波动的干扰,将这些内部随机波动和外部随机扰动考虑到系统模型中更为实际.随机动态合作博弈同时考虑策略行为、动态演化与随机因素之间的相互作用,其可能是最复杂的决策形式之一.鉴于此,对多目标动态优化中随机合作博弈的进展进行综述:首先,回顾多目标合作博弈的研究背景,给出Pareto最优性的定义和基本性质;其次,综述确定性的合作博弈;再次,分别论述随机合作博弈和平均场随机合作博弈;最后,提出随机合作博弈几个未来研究方向.     

MAGMA: a multiagent architecture for metaheuristics

In this work, we introduce a multiagent architecture called the MultiAGent Metaheuristic Architecture (MAGMA) conceived as a conceptual and practical framework for metaheuristic algorithms. Metaheuristics can be seen as the result of the interaction among different kinds of agents: The basic architecture contains three levels, each hosting one or more agents. Level-0 agents build solutions, level-1 agents improve solutions, and level-2 agents provide the high level strategy. In this framework, classical metaheuristic algorithms can be smoothly accommodated and extended. The basic three level architecture can be enhanced with the introduction of a fourth level of agents (level-3 agents) coordinating lower level agents. With this additional level, MAGMA can also describe, in a uniform way, cooperative search and, in general, any combination of metaheuristics. We describe the entire architecture, the structure of agents in each level in terms of tuples, and the structure of their coordination as a labeled transition system. We propose this perspective with the aim to achieve a better and clearer understanding of metaheuristics, obtain hybrid algorithms, suggest guidelines for a software engineering-oriented implementation and for didactic purposes. Some specializations of the general architecture will be provided in order to show that existing metaheuristics [e.g., greedy randomized adaptive procedure (GRASP), ant colony optimization (ACO), iterated local search (ILS), memetic algorithms (MAs)] can be easily described in our framework. We describe cooperative search and large neighborhood search (LNS) in the proposed framework exploiting level-3 agents. We show also that a simple hybrid algorithm, called guided restart ILS, can be easily conceived as a combination of existing components in our framework.     

A multiagent genetic algorithm for global numerical optimization.

In this paper, multiagent systems and genetic algorithms are integrated to form a new algorithm, multiagent genetic algorithm (MAGA), for solving the global numerical optimization problem. An agent in MAGA represents a candidate solution to the optimization problem in hand. All agents live in a latticelike environment, with each agent fixed on a lattice-point. In order to increase energies, they compete or cooperate with their neighbors, and they can also use knowledge. Making use of these agent-agent interactions, MAGA realizes the purpose of minimizing the objective function value. Theoretical analyzes show that MAGA converges to the global optimum. In the first part of the experiments, ten benchmark functions are used to test the performance of MAGA, and the scalability of MAGA along the problem dimension is studied with great care. The results show that MAGA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, MAGA still can find high quality solutions at a low computational cost. Therefore, MAGA has good scalability and is a competent algorithm for solving high dimensional optimization problems. To the best of our knowledge, no researchers have ever optimized the functions with 10,000 dimensions by means of evolution. In the second part of the experiments, MAGA is applied to a practical case, the approximation of linear systems, with a satisfactory result.     

A distributed Cooperative coevolutionary algorithm for multiobjective optimization

Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic sharing, and extending operator, the CCEA is capable of maintaining archive diversity in the evolution and distributing the solutions uniformly along the Pareto front. Exploiting the inherent parallelism of cooperative coevolution, the CCEA can be formulated into a distributed cooperative coevolutionary algorithm (DCCEA) suitable for concurrent processing that allows inter-communication of subpopulations residing in networked computers, and hence expedites the computational speed by sharing the workload among multiple computers. Simulation results show that the CCEA is competitive in finding the tradeoff solutions, and the DCCEA can effectively reduce the simulation runtime without sacrificing the performance of CCEA as the number of peers is increased.     

A multiobjective evolutionary algorithm toolbox for computer-aidedmultiobjective optimization

This paper presents an interactive graphical user interface (GUI) based multiobjective evolutionary algorithm (MOEA) toolbox for effective computer-aided multiobjective (MO) optimization. Without the need of aggregating multiple criteria into a compromise function, it incorporates the concept of Pareto's optimality to evolve a family of nondominated solutions distributing along the tradeoffs uniformly. The toolbox is also designed with many useful features such as the goal and priority settings to provide better support for decision-making in MO optimization, dynamic population size that is computed adaptively according to the online discovered Pareto-front, soft/hard goal settings for constraint handlings, multiple goals specification for logical "AND"/"OR" operation, adaptive niching scheme for uniform population distribution, and a useful convergence representation for MO optimization. The MOEA toolbox is freely available for download at http://vlab.ee.nus.edu.sg/-kctan/moea.htm which is ready for immediate use with minimal knowledge needed in evolutionary computing. To use the toolbox, the user merely needs to provide a simple "model" file that specifies the objective function corresponding to his/her particular optimization problem. Other aspects like decision variable settings, optimization process monitoring and graphical results analysis can be performed easily through the embedded GUIs in the toolbox. The effectiveness and applications of the toolbox are illustrated via the design optimization problem of a practical ill-conditioned distillation system. Performance of the algorithm in MOEA toolbox is also compared with other well-known evolutionary MO optimization methods upon a benchmark problem.     

一种基于偶图匹配的多目标分解进化算法

针对基于分解的多目标进化(MOEA/D)算法在选择下一代解时未考虑解和子问题之间的相对距离,可能导致算法得到的最终解多样性较差的问题,提出一种基于偶图匹配的多目标分解进化(MOEA/D-BM)算法.所提算法利用偶图匹配模型对解和子问题的相互关系进行建模,在选择下一代解的同时,考虑收敛性和多样性,以提高算法性能.通过与其他3种经典的多目标分解进化算法在多个测试函数上进行实验,验证了所提出算法的有效性.     

A multiobjective dynamic programming method for capacity expansion

This paper integrates two existing methodologies-a single-objective dynamic programming method for capacity expansion and the surrogate worth tradeoff (SWT) method for optimizing multiple objectives -into a unified schema. In particular it shows 1) how a multiobjective mixed integer programming formulation representing the multiobjective capacity expansion problem can be translated into a multiobjective dynamic programming formulation, 2) how such DP formulation can be used to generate noninferior solutions, and 3) how tradeoff information can be obtained from solutions in 2). The necessary theoretical machinery for 3) is developed. To demonstrate the computational viability of the proposed schema, an example problem is formulated and solved.     

Divergent exploration in design with a dynamic multiobjective optimization formulation

Formulation space exploration is a new strategy for multiobjective optimization that facilitates both divergent exploration and convergent optimization during the early stages of design. The formulation space is the union of all variable and design objective spaces identified by the designer as being valid and pragmatic problem formulations. By extending a computational search into the formulation space, the solution to an optimization problem is no longer predefined by any single problem formulation, as it is with traditional optimization methods. Instead, a designer is free to change, modify, and update design objectives, variables, and constraints and explore design alternatives without requiring a concrete understanding of the design problem a priori. To facilitate this process, we introduce a new vector/matrix-based definition for multiobjective optimization problems, which is dynamic in nature and easily modified. Additionally, we provide a set of exploration metrics to help guide designers while exploring the formulation space. Finally, we provide an example to illustrate the use of this new, dynamic approach to multiobjective optimization.     

A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing

The supply trajectory of electric power for submerged arc magnesia furnace determines the yields and grade of magnesia grain during the manufacture process. As the two production targets (i.e., the yields and the grade of magnesia grain) are conflicting and the process is subject to changing conditions, the supply of electric power needs to be dynamically optimized to track the moving Pareto optimal set with time. A hybrid evolutionary multiobjective optimization strategy is proposed to address the dynamic multiobjective optimization problem. The hybrid strategy is based on two techniques. The first one uses case-based reasoning to immediately generate good solutions to adjust the power supply once the environment changes, and then apply a multiobjective evolutionary algorithm to accurately solve the problem. The second one is to learn the case solutions to guide and promote the search of the evolutionary algorithm, and the best solutions found by the evolutionary algorithm can be used to update the case library to improve the accuracy of case-based reasoning in the following process. Due to the effectiveness of mutual promotion, the hybrid strategy can continuously adapt and search in dynamic environments. Two prominent multiobjective evolutionary algorithms are integrated into the hybrid strategy to solve the dynamic multiobjective power supply optimization problem. The results from a series of experiments show that the proposed hybrid algorithms perform better than their component multiobjective evolutionary algorithms for the tested problems.     

多模态多目标优化综述

随着工业生产和日常生活需求的多样化,单个解决方案己经无法满足生产生活的需求.多模态优化可以为决策者提供多个可行方案,但是早期对多模态优化的研究局限在单目标优化中.在多目标优化中也存在多模态优化问题,其存在多个全局或局部帕累托最优解集,找到这些最优解集具有重大的理论和实际意义.鉴于此,首先,介绍多模态多目标优化问题的特点...     

A multiobjective model for the dynamic location of landfills

The passage of the Resource Conservation and Recovery Act of 1976 and its subsequent renewals triggered controversies over where potentially hazardous garbage should be deposited. Such controversies originated from decade-long conflicts between the government agency seeking cost savings and the general public seeking safe environments. The controversies will go on without a settlement, unless more systematic and realistic decision-aid tools for locating landfills are developed which consider a multitude of dynamic factors affecting the location decision and then make trade-offs among them. These factors may include garbage collection services for regional communities, the explicit and hidden costs of developing landfills, transporting and disposing garbage, long-term health and safety hazards for neighboring residents and ecosystems, negative impacts on the regional economy, equity concerns and so forth. As an effective decision-aid tool that can incorporate these conflicting factors, we develop a dynamic (multiperiod), multiobjective mixed integer programming model. The model is tested in a hypothetical case resembling a real world scenario and the results are interpreted to provide insights into the multiobjective and dynamic nature of the model. Efficient alternatives are generated, using the weighting method, and reduced using a filtering method.     

A parallel double-level multiobjective evolutionary algorithm for robust optimization

Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.