A number of evolutionary Computations (ECs) have been developed for solving Multimodal Function Optimization Problems (MFOPs) [1]. Some of the well-known ones are: Fitness Sharing [2], Sequential Niching [3], Simple S...
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(纸本)1889335185
A number of evolutionary Computations (ECs) have been developed for solving Multimodal Function Optimization Problems (MFOPs) [1]. Some of the well-known ones are: Fitness Sharing [2], Sequential Niching [3], Simple Subpopulation Schemes [4] and Co-evolutionary Shared Niching [5]. These ECs have shown the capability of solving MFOPs, but have introduced one or more parameters that cannot be easily set without prior knowledge of the fitness landscape. Moreover, a priori knowledge of a particular MFOP may not always be readily available. In this work, we describe a set of parallel and distributed ECs that are capable of locating all the peaks in a MFOP without using parameters that require specific topological information. This paper also provides a performance comparison between three approaches to solving MFOPs: Fitness Sharing, parallel EC and distributed EC.
Contract manufacturing is one of the most prevalent supply chain models recently. Formation of contract manufacturing supply chain network tends to be more dynamic as establishment of partnership is based on the contr...
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Contract manufacturing is one of the most prevalent supply chain models recently. Formation of contract manufacturing supply chain network tends to be more dynamic as establishment of partnership is based on the contracted orders. Optimization of contract manufacturing supply chain networks is much more difficult than traditional production networks. The objective of this paper is to propose a self-organization framework for contract manufacturing supply chain. We define several constraint satisfaction genetic operator to model key activities such as changing upstream or downstream partners or reallocation of production capacities among partners in contract manufacturing supply chains. Our results include: (a)a mathematical model for contract manufacturing supply chain network, (b) a self-organization framework based on evolutionary approach, (c) comparison on different types of supply chain alliance and (d) a constraint satisfaction algorithm.
The need for supporting CSCW applications with heterogeneous and varying user requirements calls for adaptive and reconfigurable schedulers accommodating a mixture of real-time, proportional share, fixed priority and ...
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A multi-objective evolutionary algorithm is used to deter- mine the membership function distribution within the outer loop control system of a non-linear missile autopilot using lateral acceleration con- trol. This pr...
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The proceedings contain 66 papers from the Applications on evolutionary Computing - EvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Proceedings. The topics discussed include: a fuzzy vit...
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The proceedings contain 66 papers from the Applications on evolutionary Computing - EvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Proceedings. The topics discussed include: a fuzzy viterbi algorithm for improved sequence alignment and searching of proteins;order preserving clustering over multiple time course experiments;neural networks and temporal gene expression data;bayesian learning with local support vector machines for cancer classification with gene expression data;syntactic approach to predict membrane spanning regions of transmembrane proteins;and an evolutionary approach for motif discovery and transmembrane protein classification.
In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to han...
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In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness. (C) 2014 Elsevier Ltd. All rights reserved.
Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of ...
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Artificial immune systems are a kind of new computational intelligence methods which draw inspiration from the human immune system. Most immune system inspired optimization algorithms are based on the applications of clonal selection and hypermutation, and known as clonal selection algorithms. These clonal selection algorithms simulate the immune response process based on principles of Darwinian evolution by using various forms of hypermutation as variation operators. The generation of new individuals is a form of the trial and error process. It seems very wasteful not to make use of the Baldwin effect in immune system to direct the genotypic changes. In this paper, based on the Baldwin effect. an improved clonal selection algorithm, Baldwinian Clonal Selection Algorithm, termed as BCSA, is proposed to deal with optimization problems. BCSA evolves and improves antibody population by four operators, clonal proliferation, Baldwinian learning, hypermutation, and clonal selection. It is the first time to introduce the Baldwinian learning into artificial immune systems. The Baldwinian learning operator simulates the learning mechanism in immune system by employing information from within the antibody population to alter the search space. It makes use of the exploration performed by the phenotype to facilitate the evolutionary search for good genotypes. In order to validate the effectiveness of BCSA, eight benchmark functions, six rotated functions, six composition functions and a real-world problem, optimal approximation of linear systems are solved by BCSA, successively. Experimental results indicate that BCSA performs very well in solving most of the test problems and is an effective and robust algorithm for optimization. (C) 2009 Elsevier Inc. All rights reserved.
This work introduces and formalizes the Flying Tourist Problem (FTP), whose goal is to find the best schedule, route, and set of flights for any given unconstrained multi-city flight request. To solve the FTP, the dev...
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This work introduces and formalizes the Flying Tourist Problem (FTP), whose goal is to find the best schedule, route, and set of flights for any given unconstrained multi-city flight request. To solve the FTP, the developed work proposes a methodology that allows an efficient resolution of this rather demanding problem. This strategy uses different heuristics and meta-heuristic optimization algorithms, allowing the identification of solutions in real-time, even for large problem instances. The implemented system was evaluated using different criteria, including the provided gains (in terms of total flight price and duration) and its performance compared to other similar systems. The obtained results show that the developed optimization system consistently presents solutions that are up to 35% cheaper (or 60% faster) than those developed by simpler heuristics. Furthermore, when comparing the developed system to the only publicly available (but not-disclosed) alternative for flight search, it was shown that it provides the best-recommended and the cheapest solutions, respectively 74% and 95% of the times, allowing the user to save time and money. (C) 2019 Elsevier Ltd. All rights reserved.
This paper proposes a novel hybrid approach to solve the DNA sequence assembly problem by combining particle swarm optimization and iterative local search algorithms. One of the vital challenges in DNA sequence assemb...
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This paper proposes a novel hybrid approach to solve the DNA sequence assembly problem by combining particle swarm optimization and iterative local search algorithms. One of the vital challenges in DNA sequence assembly is to arrange a long genome sequence that consists of millions of fragments in accurate order. This is an NP- hard combinatorial optimization problem. The prominence of this paper is to demonstrate how this hybrid algorithms scheme can improve the performance of fragment assembly process. Incorporating iterative local search heuristics in particle swarm optimization algorithm efficiently assembles the fragments by maximizing the overlap score. The performances of the proposed hybrid algorithm were compared with the variants of Particle Swarm Optimization algorithms and other known methodologies. The experimental results show that the proposed hybrid approach produces better results than the other techniques when tested with different sized well-known benchmark instances.
-This paper provides a computational methodology based on monarch butterfly optimization (MBO) to find a solution to the problem of cost-based unit commitment (CBUC). The binary variables of unit commitment problems a...
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-This paper provides a computational methodology based on monarch butterfly optimization (MBO) to find a solution to the problem of cost-based unit commitment (CBUC). The binary variables of unit commitment problems are handled by modifying the continuous-time nature of the monarch butterfly algorithm. Thermal unit generation, uptime, downtime, ramp rate limits as well as system reserve are considered in the test systems. The computational approach has many parts that not only minimize the cost function but also handle the mixed constraints of the commitment problem. The effect of thermal turbine valve-point loading is also taken into consideration. The computational technique has been used to solve a ten-unit original system and five scaled-up adaptations obtained from this base system. The results obtained are in agreement with the recent results available in the literature. Comparative analysis shows the effectiveness of the proposed MBO-based solution methodology in terms of operating costs and execution time in relation to other techniques.
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