Multi-objective evolutionary algorithms
A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls
... for multi-objective problems were presented in Jozefowiez et ...proposed algorithms to solve them. A survey of the state of the art of the multi-objective evolutionary ...
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A HYBRID MULTI-OBJECTIVE BAYESIAN ESTIMATION OF DISTRIBUTION ALGORITHM THESIS
... distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising ...of multi-objective ...
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Preference-guided evolutionary algorithms for optimization with many objectives
... Multi-objective Evolutionary Algorithms, mainly the Pareto-based ones (like NSGA-II and SPEA2), were very successful when solving a lot of real world problems (see [22] for some examples), ...
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Strength Pareto Evolutionary Algorithm based Multi-Objective Optimization for Shortest Path Routing Problem in Computer Networks
... new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA), is presented in this paper to solve the shortest path routing ...a multi-objective mathematical programming ...
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Non-dominated Sorting Genetic Algorithms for Heterogeneous Embedded System Design
... The design of embedded systems is particularly driven by multiple and conflicting objectives like cost and reliability. Here the user is never satisfied for a trivial solution of both maximal cost and reliability. In ...
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A Convergence Indicator for Multi-Objective Optimisation Algorithms
... the evolutionary algorithms (EAs) are used to obtain approximate solutions of multi- objective optimisation problems (MOP) and these EAs are called multi-objective ...
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Abstract — The physical topology design (PTD) of optical
... Araújo et al. [9] compared five different multi-objective evolutionary algorithms applied to design the physical topology of optical networks. In [9], Araújo et al. used a random generator ...
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Multi-objective optimization with Kriging surrogates using “moko”, an open source package
... efficient algorithms for dealing with multi-objective optimization, most of them based on evolutionary techniques (evolutionary multi-objective optimization ...
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GeDEA-II: A Novel Evolutionary Algorithm for Multi-Objective Optimization Problems Based on the Simplex Crossover and The Shrink Mutation
... proposed algorithms, have been carried out on various test ...of objective functions evaluations, a very useful feature when considering its application to real-world engineering ...
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Adaptive complex system modeling for realistic modern ground warfare simulation analysis based on evolutionary multi-objective meta-heuristic techniques
... Search algorithms [59] (and their many variants) work by taking a starting solution , and then searching the candidate solutions in for one that performs better than ...Such algorithms (often referred to as ...
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Multi-objective metaheuristic algorithms for the resource-constrained project scheduling problem with precedence relations
... Several multi-objective optimization methods can be found in literature to solve this class of ...the objective function vector into a scalar objective function, as it is the case of the ...
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Multi-objective pareto-efficient algorithms for recommender systems
... In this work we use a second version of the strength Pareto evolutionary algorithm (SPEA-2), proposed by Zitzler et al. [2001]; Zitzler and Thiele [1999]. The The aim is to find or approximate the Pareto-optimal ...
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Modeling and Optimization of Precedence-Constrained Production Sequencing and Scheduling Using Multi-Objective Genetic Algorithms
... For PCSP in supply chain, Moon et al. [14] proposed GA with a priority-based encoding method to solve the scheduling problem. For problems with sequence-dependent changeover cost and precedence constraints, He & ...
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Soft combination of local models in a multi-objective framework
... and in combining the best performing models associated to each objective in such a way that the strength of each individual model used in the combination is exploited. Such an approach attempts to improve the ...
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Soft combination of local models in a multi-objective framework
... – Events selection. Within a modular approach, which presumes switching between different models, these events should correspond to different aspects of the system behaviour. Consequently, they should refer to different ...
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Procedural Optimization Models for Multiobjective Flexible JSSP
... emerging when the set of interacting agents is globally considered. Hence, the colony proves to be an intelligent entity when solves a problem, while the individual agents do not have this ability. Most of nature ...
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MULTI OBJECTIVE ECONOMIC DISPATCH USING PARETO FRONTIER DIFFERENTIAL EVOLUTION
... Multi Objective Economic dispatch (MOED) problem has gained recent attention due to the deregulation of power industry and environmental ...the Multi Objective economic dispatch problem ...
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Multi-Objective Fuzzy Linear Programming In Agricultural Production Planning
... Rice is the staple food for more than half of the human population, and in Asia alone more than 2 billion people depend on rice and its products for their food intake. Rice is the single most important crop occupying 34 ...
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A New Strategy to Optimize the Load Migration Process in Cloud Environment
... Load Balancing Mechanism based on Ant colony and Complex Network Theory (ACCLB): It is presented in a federation of open CC with the purpose of overcoming complexity and problems of dynamic load balancing which use ...
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A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement
... In the case of ACO algorith ms the theoretical analyses of their runtime behavior has been started only recently. We increase the theoretical understanding of ACO algorithms by investigating their runtime behavior ...
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