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 r...
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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 multiobjectiveevolutionary 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.
Environmental adaptation method is one of the evolutionaryalgorithms for solving single objective optimization problems. Although the algorithm converges very fast and produces diversified solutions, there are three ...
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Environmental adaptation method is one of the evolutionaryalgorithms 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 multiobjectivealgorithms. Extensive experimental results demonstrated that the pMOEAM method can obtain effective and feasible solutions for EED problem.
This paper presents the use of a multiobjectiveevolutionary algorithm, namely population-based incremental learning (PBIL), for the design/optimization of a splayed pin-fin heat sink. An innovative design strategy ba...
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This paper presents the use of a multiobjectiveevolutionary 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.
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. ...
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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.
This article provides a short introduction to the evolutionarymultiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been d...
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This article provides a short introduction to the evolutionarymultiobjective 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.
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 ...
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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 evolutionaryalgorithms 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.
This paper presents two surrogate-assisted optimization strategies for structural constrained multiobjective optimization. The optimization strategies are based on hybridization of multiobjective population-based incr...
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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).
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 ...
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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.
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 implem...
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In this paper we propose the use a multiobjectiveevolutionary optimization algorithm to solve the regenerators placement problem. The optical network performance and the capital costs are used as optimization objecti...
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ISBN:
(纸本)9781424477982
In this paper we propose the use a multiobjectiveevolutionary 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.
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