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A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization

INFORMATION SCIENCES 2007年第2期177卷 632-654页

作者： Ripon, Kazi Shah Nawaz Kwong, Sam Man, K. F. City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

This paper presents a real jumping gene genetic algorithm (RJGGA) as an enhancement of the jumping gene genetic algorithm (JGGA) [T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, A jumping gene algorithm for multiobjective resource management in wideband CDMA systems, The Computer Journal 48 (6) (2005) 749-768;T.M. Chan, K.F. Man, K.S. Tang, S. Kwong, multiobjective optimization of radio-to-fiber repeater placement using a jumping gene algorithm, in: Proceedings of the IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong, 2005, pp. 291-296;K.F. Man, T.M. Chan, K.S. Tang, S. Kwong, Jumping-genes in evolutionary computing, in: Proceedings of the IEEE IECON'2004, Busan, 2004, pp, 1268-1272]. JGGA is a relatively new multiobjective evolutionary algorithm (MOEA) that imitates a jumping gene phenomenon discovered by Nobel Laureate McClintock during her work on the corn plants. The main feature of JGGA is that it only has a simple operation in which a transposition of gene(s) is induced within the same or another chromosome in the genetic algorithm (GA) framework. In its initial formulation, the search space solutions are binary-coded and it inherits the customary problems of conventional binary-coded GA (BCGA). This issue motivated us to remodel the JGGA into RJGGA. The performance of RJGGA has been compared to other MOEAs using some carefully chosen benchmark test functions. It has been observed that RJGGA is able to generate non-dominated solutions with a wider spread along the Pareto-optimal front and better address the issues regarding convergence and diversity in multiobjective optimization. (C) 2006 Elsevier Inc. All rights reserved.

关键词： multiobjective evolutionary algorithms jumping genes jumping gene genetic algorithm binary-coding real-coding benchmark test functions performance metrics

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multiobjective environmental adaptation method for solving environmental/economic dispatch problem

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evolutionary INTELLIGENCE 2019年第2期12卷 305-319页

作者： Singh, Tribhuvan Mishra, Krishn Kumar Ranvijay Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Dept Allahabad Uttar Pradesh India

Environmental adaptation method is one of the evolutionary algorithms for solving single objective optimization problems. Although the algorithm converges very fast and produces diversified solutions, there are three weaknesses in it. In this paper, first we have given the solutions to resolve these weaknesses and then we have extended the modified method to deal with multiple conflicting objectives simultaneously. A permutation-based multiobjective environmental adaptation method (pMOEAM) has been suggested to solve the environmental/economic dispatch (EED) problem of the power system. In this paper, total generation cost and environmental emission have been taken as two objectives that need to be minimized simultaneously while meeting the load demand under equality and inequality constraints. Three test systems are considered to evaluate the performance of the proposed algorithm. The performance of the suggested algorithm is compared against five multiobjective algorithms. Extensive experimental results demonstrated that the pMOEAM method can obtain effective and feasible solutions for EED problem.

关键词： Environmental/economic dispatch problem Environmental adaptation method multiobjective evolutionary algorithms multiobjective optimization problems

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multiobjective evolutionary OPTIMIZATION OF SPLAYED PIN-FIN HEAT SINK

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2011年第4期5卷 553-565页

作者： Kanyakam, Siwadol Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40000 Thailand

This paper presents the use of a multiobjective evolutionary algorithm, namely population-based incremental learning (PBIL), for the design/optimization of a splayed pin-fin heat sink. An innovative design strategy based on evolutionary optimization is presented for the enhancement of the heat sink's performance. The design problem is to simultaneously minimize the junction temperature and the fan pumping power of the heat sink. Design variables determine the sizes and geometry of the heat sink. The new encoding/decoding process of the design variables, using surface spline interpolation, is detailed. There are 24 design parameters including fin number, fin height, fin width, heat sink base thickness and size, fin bevel, and inlet air velocity. Manufacturing tolerances, such as the heat sink aspect ratio and the device free space, are taken as design constraints. Computational fluid dynamics is used for the objective function evaluation. Numerical results show that PBIL is a powerful tool for the optimal design of a splayed pin-fin heat sink. This new design approach results in heat sinks with variation in fin heights, and it is said to be superior to available commercial splayed pin-fin heat sinks.

关键词： splayed pin-fin heat sink multiobjective evolutionary algorithms computational fluid dynamics population-based incremental learning thermal performance

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M-SPOT: A Hybrid multiobjective evolutionary Algorithm for Node Placement in Wireless Sensor Networks 32

M-SPOT: A Hybrid Multiobjective Evolutionary Algorithm for N...

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32nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Perez, Alfredo J. Columbus State Univ TSYS Sch Comp Sci Columbus GA 31907 USA

ISBN: (纸本)9781538653951

We address the problem of the placement of static sensors and relays to monitor specific locations in an area assuming a single-tiered wireless sensor network model with limited communication and sensing constraints. We present a multiobjective optimization model with two conflicting objectives: total number of devices used in the placement and total energy dissipated by the placement. To optimize the model, we propose the multiobjective Sensor Placement Optimizer (M-SPOT) algorithm, which is a hybrid evolutionary algorithm that combines the Non-Sorting Genetic Algorithm 2 (NSGA2) algorithm with local search heuristics. We evaluate the performance of M-SPOT by simulating the placement of sensors and relays. We found that the utilization of local search heuristics greatly contribute to find better placements when compared to the NSGA2 algorithm.

关键词： Relay Node Placement Energy Wireless Sensor Networks multiobjective Optimization Hybrid algorithms multiobjective evolutionary algorithms

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evolutionary multiobjective Optimization in Materials Science and Engineering

Evolutionary Multiobjective Optimization in Materials Scienc...

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2nd International Conference on Neural Network and Genetic algorithms in Materials Science and Engineering

作者： Coello Coello, Carlos A. Landa Becerra, Ricardo IPN CINVESTAV Dept Comp Evolutionary Computat Grp Mexico City 07360 DF Mexico

This article provides a short introduction to the evolutionary multiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been developed, from a historical perspective. In the second part of the article, some representative applications within materials science and engineering are reviewed. In the final part of the article, some potential areas for future research in this area are briefly described.

关键词： Alloys design Applications of evolutionary algorithms Casting of metals Cold rolling evolutionary algorithms evolutionary multiobjective optimization Heat treatment Hot rolling Iron-making Metaheuristics multiobjective evolutionary algorithms multiobjective optimization Polymerization Reheating Steel-making

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Component-based thinking in designing interactive multiobjective evolutionary methods

Component-based thinking in designing interactive multiobjec...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Larraga, Giomara Miettinen, Kaisa Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland

ISBN: (纸本)9798400701207

multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. They have many Pareto optimal solutions representing different trade-offs, and a decision-maker needs to find the most preferred one. Although most multiobjective evolutionary algorithms approximate the Pareto optimal set, their variants incorporate preference information to focus on a subset of solutions that interest the decision-maker. Interactive methods allow decision-makers to provide preference information iteratively during the solution process, enabling them to learn about available solutions and their preferences' feasibility. Nevertheless, most interactive evolutionary methods do not sufficiently support the decision-maker in finding the most preferred solution and may be cognitively too demanding. We propose a framework for designing and implementing interactive evolutionary methods. It contains algorithmic components based on similarities in the structure of existing preference-based evolutionary algorithms and decision-makers' needs during interaction. The components can be combined in different ways to create new interactive methods or to instantiate the existing ones. We show an example of the implementation of the proposed framework composed of three elements: a graphical user interface, a database, and a set of algorithmic components. The resulting software can be utilized to develop new methods and increase their usability in real-world applications.

关键词： multiobjective optimization interactive methods multiobjective evolutionary algorithms decision-making

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Surrogate-Assisted evolutionary Optimizers for multiobjective Design of a Torque Arm Structure

Surrogate-Assisted Evolutionary Optimizers for Multiobjectiv...

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2nd International Conference on Engineering Design and Optimization (ICEDO 2011)

作者： Pholdee, Nantiwat Bureerat, Sujin Khon Kaen Univ Fac Engn Dept Mech Engn Khon Kaen 40002 Thailand

ISBN: (纸本)9783037852590

This paper presents two surrogate-assisted optimization strategies for structural constrained multiobjective optimization. The optimization strategies are based on hybridization of multiobjective population-based incremental learning (MOPBIL) and radial-basis function (RBF) interpolation. The first strategy uses MOPBIL for generating training points while the second strategy uses a Latin hypercube sampling (LHS) technique. The design case study is the shape and sizing design of a torque arm structure. A design problem is set to minimize structural mass and displacement while constraints include stresses due to three different load cases. Structural analysis is carried out by means of a finite element approach. The design problem is then tackled by the proposed surrogate-assisted design strategies. Numerical results show that the use of MOPBIL for generating training points is superior to the use of LHS based on a hypervolume indicator and root mean square error (RMSE).

关键词： multiobjective population-based incremental learning Latin Hypercube Sampling Surrogate Model Structural Constrained Optimization multiobjective evolutionary algorithms

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A multiobjective evolutionary algorithm for QoS-aware planning in heterogeneous computing systems 40

A multiobjective evolutionary algorithm for QoS-aware planni...

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40th Latin American Computing Conference (CLEI)

作者： Murana, Jonathan Iturriaga, Santiago Nesmachnow, Sergio Univ Republica Fac Ingn Ctr Calculo Herrera & Reissig 565 Montevideo 11300 Uruguay

ISBN: (纸本)9781479961306

This article presents the application of a parallel evolutionary algorithm for solving a multiobjective version of the task scheduling problem in heterogeneous computing infrastructures (cluster and grid systems). In real-life scenarios, the scheduling problem must take into account the needs of both service providers and users. Thus, the multiobjective version of the problem solved in this article is relevant to find schedules with accurate trade-off values between the quality-of-service levels (given by deadlines for the tasks) and minimizing the execution time required for a set of tasks submitted to the system. The problem is studied over scenarios with dimensions that represent realistic nowadaus computing infrastructures, and a parallel evolutionary algorithm is introduced to efficiently solve the problem. The experimental analysis considering both problem objectives demonstrate that the proposed algorithm is able to compute high-quality solutions for the problem, with accurate trade-off values between system utilization and quality of service, outperforming a set of well-known deterministic heuristics for hterogeneous computing scheduling.

关键词： scheduling multiobjective evolutionary algorithms heterogeneous computing

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Enhancing Mass Customization Manufacturing: multiobjective Metaheuristic algorithms for Flow Shop Production in Smart Industry

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SN Computer Science 2024年第6期5卷 782页

作者： Rossit, Diego Rossit, Daniel Nesmachnow, Sergio Engineering Department and INMABB Universiad Nacional del Sur and CONICET 1253 Avenida Alem Buenos Aires Bahía Blanca 8000 Argentina Facultad de Ingeniería Universidad de la República Julio Herrera y Reissig 565 Montevideo Montevideo 11300 Uruguay

The current landscape of massive production industries is undergoing significant transformations driven by emerging customer trends and new smart manufacturing technologies. One such change is the imperative to implement mass customization, wherein products are tailored to individual customer specifications while still ensuring cost efficiency through large-scale production processes. These shifts can profoundly impact various facets of the industry. This study focuses on the necessary adaptations in shop-floor production planning. Specifically, it proposes the use of efficient evolutionary algorithms to tackle the flowshop with missing operations, considering different optimization objectives: makespan, weighted total tardiness, and total completion time. An extensive computational experimentation is conducted across a range of realistic instances, encompassing varying numbers of jobs, operations, and probabilities of missing operations. The findings demonstrate the competitiveness of the proposed approach and enable the identification of the most suitable evolutionary algorithms for addressing this problem. Additionally, the impact of the probability of missing operations on optimization objectives is discussed. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Flowshop scheduling problem Industry 4.0 Mass customization Missing operations multiobjective evolutionary algorithms Smart industry

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multiobjective Sparse Regeneration Placement Algorithm in Optical Networks Considering Network Performance and CAPEX

Multiobjective Sparse Regeneration Placement Algorithm in Op...

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12th International Conference on Transparent Optical Networks (ICTON)

作者： Chaves, Daniel A. R. Ayres, Caio F. C. L. C. Carvalho, Renan V. B. Pereira, Helder A. Bastos-Filho, Carmelo J. A. Martins-Filho, Joaquim F. Univ Fed Pernambuco Dept Elect & Syst Recife PE Brazil Univ Pernambuco Polytech Sch Pernambuco Recife PE Brazil

ISBN: (纸本)9781424477982

In this paper we propose the use a multiobjective evolutionary optimization algorithm to solve the regenerators placement problem. The optical network performance and the capital costs are used as optimization objectives. The blocking probability is used as the network performance indicator, which is evaluated by network simulations with an impairment aware routing and wavelength assignment algorithm, whereas the total regenerators cost is the total number of regenerators deployed in the network. Our algorithm finds which network nodes should have regeneration capability and the number of regenerators in each of these regenerating nodes. Our proposal outperformed other well known algorithms found in the literature such as NDF and SQP.

关键词： multiobjective evolutionary algorithms Sparse Regeneration Translucent Optical Networks

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