The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Qingfu Zhang - One of the best experts on this subject based on the ideXlab platform.

  • constrained subProblems in a decomposition based Multiobjective evolutionary algorithm
    IEEE Transactions on Evolutionary Computation, 2016
    Co-Authors: Luping Wang, Qingfu Zhang, Aimin Zhou, Maoguo Gong, Licheng Jiao
    Abstract:

    A decomposition approach decomposes a Multiobjective Optimization Problem into a number of scalar objective Optimization subProblems. It plays a key role in decomposition-based Multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based Multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subProblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

  • are all the subProblems equally important resource allocation in decomposition based Multiobjective evolutionary algorithms
    IEEE Transactions on Evolutionary Computation, 2016
    Co-Authors: Aimin Zhou, Qingfu Zhang
    Abstract:

    Decomposition-based Multiobjective evolutionary algorithms (MOEAs) decompose a Multiobjective Optimization Problem into a set of scalar objective subProblems and solve them in a collaborative way. A naive way to distribute computational effort is to treat all the subProblems equally and assign the same computational resource to each subProblem. This paper proposes a generalized resource allocation (GRA) strategy for decomposition-based MOEAs by using a probability of improvement vector. Each subProblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subProblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed GRA strategy have been conducted.

  • decomposition of a Multiobjective Optimization Problem into a number of simple Multiobjective subProblems
    IEEE Transactions on Evolutionary Computation, 2014
    Co-Authors: Fangqing Gu, Qingfu Zhang
    Abstract:

    This letter suggests an approach for decomposing a Multiobjective Optimization Problem (MOP) into a set of simple Multiobjective Optimization subProblems. Using this approach, it proposes MOEA/D-M2M, a new version of Multiobjective Optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subProblems in a collaborative way. Each subProblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in Multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better.

  • community detection in networks by using Multiobjective evolutionary algorithm with decomposition
    Physica A-statistical Mechanics and Its Applications, 2012
    Co-Authors: Maoguo Gong, Qingfu Zhang, Licheng Jiao
    Abstract:

    Abstract Community structure is an important property of complex networks. Most Optimization-based community detection algorithms employ single Optimization criteria. In this study, the community detection is solved as a Multiobjective Optimization Problem by using the Multiobjective evolutionary algorithm based on decomposition. The proposed algorithm maximizes the density of internal degrees, and minimizes the density of external degrees simultaneously. It can produce a set of solutions which can represent various divisions to the networks at different hierarchical levels. The number of communities is automatically determined by the non-dominated individuals resulting from our algorithm. Experiments on both synthetic and real-world network datasets verify that our algorithm is highly efficient at discovering quality community structure.

  • Multiobjective evolutionary algorithms a survey of the state of the art
    Swarm and evolutionary computation, 2011
    Co-Authors: Aimi Zhou, Shizheng Zhao, P N Sugantha, Qingfu Zhang
    Abstract:

    Abstract A Multiobjective Optimization Problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, Multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal Problems, constraint handling and MOEAs, computationally expensive Multiobjective Optimization Problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark Problems, performance indicators, and applications. In addition, some future research issues are also presented.

Mousumi Basu - One of the best experts on this subject based on the ideXlab platform.

  • economic environmental dispatch of fixed head hydrothermal power systems using nondominated sorting genetic algorithm ii
    Applied Soft Computing, 2011
    Co-Authors: Mousumi Basu
    Abstract:

    This paper presents nondominated sorting genetic algorithm-II for economic environmental dispatch of fixed head hydrothermal power systems. The Problem is formulated as a nonlinear constrained Multiobjective Optimization Problem. Nondominated sorting genetic algorithm-II is proposed to handle economic environmental dispatch of fixed head hydrothermal power systems as a true Multiobjective Optimization Problem with competing and noncommensurable objectives. Numerical results of two test systems demonstrate the capabilities of the proposed approach. Results obtained from the proposed approach have been compared with those obtained from strength pareto evolutionary algorithm 2, Multiobjective differential evolution and previously obtained results.

  • economic environmental dispatch using multi objective differential evolution
    Applied Soft Computing, 2011
    Co-Authors: Mousumi Basu
    Abstract:

    Economic environmental dispatch (EED) is an important Optimization task in fossil fuel fired power plant operation for allocating generation among the committed units such that fuel cost and emission level are optimized simultaneously while satisfying all operational constraints. It is a highly constrained Multiobjective Optimization Problem involving conflicting objectives with both equality and inequality constraints. In this paper, multi-objective differential evolution has been proposed to solve EED Problem. Numerical results of three test systems demonstrate the capabilities of the proposed approach. Results obtained from the proposed approach have been compared to those obtained from pareto differential evolution, nondominated sorting genetic algorithm-II and strength pareto evolutionary algorithm 2.

Carlos Coello A Coello - One of the best experts on this subject based on the ideXlab platform.

  • on the influence of the number of objectives on the hardness of a Multiobjective Optimization Problem
    IEEE Transactions on Evolutionary Computation, 2011
    Co-Authors: Oliver Schutze, Adriana Lara, Carlos Coello A Coello
    Abstract:

    In this paper, we study the influence of the number of objectives of a continuous Multiobjective Optimization Problem on its hardness for evolution strategies which is of particular interest for many-objective Optimization Problems. To be more precise, we measure the hardness in terms of the evolution (or convergence) of the population toward the set of interest, the Pareto set. Previous related studies consider mainly the number of nondominated individuals within a population which greatly improved the understanding of the Problem and has led to possible remedies. However, in certain cases this ansatz is not sophisticated enough to understand all phenomena, and can even be misleading. In this paper, we suggest alternatively to consider the probability to improve the situation of the population which can, to a certain extent, be measured by the sizes of the descent cones. As an example, we make some qualitative considerations on a general class of uni-modal test Problems and conjecture that these Problems get harder by adding an objective, but that this difference is practically not significant, and we support this by some empirical studies. Further, we address the scalability in the number of objectives observed in the literature. That is, we try to extract the challenges for the treatment of many-objective Problems for evolution strategies based on our observations and use them to explain recent advances in this field.

  • increasing selective pressure towards the best compromise in evolutionary Multiobjective Optimization the extended nosga method
    Information Sciences, 2011
    Co-Authors: Eduardo Fernandez, Edy Lopez, Fernando Lopez, Carlos Coello A Coello
    Abstract:

    Most current approaches in the evolutionary Multiobjective Optimization literature concentrate on adapting an evolutionary algorithm to generate an approximation of the Pareto frontier. However, finding this set does not solve the Problem. The decision-maker still has to choose the best compromise solution out of that set. Here, we introduce a new characterization of the best compromise solution of a Multiobjective Optimization Problem. By using a relational system of preferences based on a multicriteria decision aid way of thinking, and an outranked-based dominance generalization, we derive some necessary and sufficient conditions which describe satisfactory approximations to the best compromise. Such conditions define a lexicographic minimum of a bi-objective Optimization Problem, which is a map of the original one. The NOSGA-II method is a NSGA-II inspired efficient way of solving the resulting mapped Problem.

  • an evolutionary approach to solve a novel mechatronic Multiobjective Optimization Problem
    Advances in Metaheuristics for Hard Optimization, 2007
    Co-Authors: Efren Mezuramontes, Edgar Alfredo Portillaflores, Carlos Coello A Coello, Jaime Alvarezgallegos, Carlos A Cruzvillar
    Abstract:

    In this chapter, we present an evolutionary approach to solve a novelmechatronic design Problemof a pinion-rack continuously variable transmission (CVT).This Problem is stated as a Multiobjective Optimization Problem, because we concurrently optimize the mechanical structure and the controller performance, in order to produce mechanical, electronic and control flexibility for the designed system. The Problem is solved first with a mathematical programming technique called the goal attainment method. Based on some shortcomings found, we propose a differential evolution (DE)-based approach to solve the aforementioned Problem. The performance of both approaches (goal attainment and the modified DE) are compared and discussed, based on quality, robustness, computational time and implementation complexity. We also highlight the interpretation of the solutions obtained in the context of the application.

  • solving hard Multiobjective Optimization Problems using e constraint with cultured differential evolution
    Parallel Problem Solving from Nature, 2006
    Co-Authors: Ricardo Landa Becerra, Carlos Coello A Coello
    Abstract:

    In this paper, we propose the use of a mathematical programming technique called the e-constraint method, hybridized with an evolutionary single-objective optimizer: the cultured differential evolution. The e-constraint method uses the cultured differential evolution to produce one point of the Pareto front of a Multiobjective Optimization Problem at each iteration. This approach is able to solve difficult Multiobjective Problems, relying on the efficiency of the single-objective optimizer, and on the fact that none of the two approaches (the mathematical programming technique or the evolutionary algorithm) are required to generate the entire Pareto front at once. The proposed approach is validated using several difficult Multiobjective test Problems, and our results are compared with respect to a multi-objective evolutionary algorithm representative of the state-of-the-art in the area: the NSGA-II.

Michael K Lindell - One of the best experts on this subject based on the ideXlab platform.

  • water as warning medium food grade dye injection for drinking water contamination emergency response
    Journal of Water Resources Planning and Management, 2014
    Co-Authors: Amin Rasekh, Kelly Brumbelow, Michael K Lindell
    Abstract:

    AbstractOnce contamination of a drinking water distribution system is suspected or known, emergency managers may take different actions in response to the perceived state of the system. This study explores performance of a novel protective response action—injection of food-grade dye directly into drinking water—for minimization of health impacts as a contamination threat unfolds. Dye injection acts as an alerting mechanism that discourages public consumption of potentially contaminated water. Considering the uncertainties in threat observations and the imperfection in system understanding, however, the action has potential for costly false alarms. These could occur when contamination has indeed happened but population segments residing in safe regions are mistakenly alerted or when suspected indications of contamination occurrence turn out to be entirely wrong. The emergency response is, thus, formulated as a Multiobjective Optimization Problem for the minimization of risks to life with minimum public war...

Amin Rasekh - One of the best experts on this subject based on the ideXlab platform.

  • water as warning medium food grade dye injection for drinking water contamination emergency response
    Journal of Water Resources Planning and Management, 2014
    Co-Authors: Amin Rasekh, Kelly Brumbelow, Michael K Lindell
    Abstract:

    AbstractOnce contamination of a drinking water distribution system is suspected or known, emergency managers may take different actions in response to the perceived state of the system. This study explores performance of a novel protective response action—injection of food-grade dye directly into drinking water—for minimization of health impacts as a contamination threat unfolds. Dye injection acts as an alerting mechanism that discourages public consumption of potentially contaminated water. Considering the uncertainties in threat observations and the imperfection in system understanding, however, the action has potential for costly false alarms. These could occur when contamination has indeed happened but population segments residing in safe regions are mistakenly alerted or when suspected indications of contamination occurrence turn out to be entirely wrong. The emergency response is, thus, formulated as a Multiobjective Optimization Problem for the minimization of risks to life with minimum public war...

  • food grade dye injection a novel alerting mechanism for consumers warning of drinking water contamination
    World Environmental and Water Resources Congress 2012: Crossing Boundaries, 2012
    Co-Authors: Amin Rasekh
    Abstract:

    In the event of contamination of a water distribution system, effective actions must be taken by the utility operators to protect public health. Food-grade dye injection is a public notification means that discourages people from consuming potentially contaminated water. This article investigates applicability of this response action and demonstrates its performance on a realistic water distribution system. The emergency response is formulated as a Multiobjective Optimization Problem for the minimization of risks to life with minimum false public warning and execution cost. Dye injection locations, loading, and timing are treated as Optimization decision variables and sensitivity analyses are performed on number of injection locations, response delay, and contamination event characteristics. The results of this study indicate the proposed modeling framework will be useful for the management of contamination events for diverse types of contaminant agents. Moreover, it does not cause interruption of firefighting and unintended damages to the system.