Titlebook: Metaheuristics for Dynamic Optimization; Enrique Alba,Amir Nakib,Patrick Siarry Book 2013 Springer-Verlag Berlin Heidelberg 2013 Computati

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Dynamic Multi-Objective Optimization Using PSO,ve, but many goals that are in conflict with one another - improvement in one goal leads to deterioration of another. Therefore, when solving dynamic multi-objective optimization problem, an algorithm attempts to find the set of optimal solutions, referred to as the Pareto-optimal front. Each dynami

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Ant Colony Based Algorithms for Dynamic Optimization Problems, used and assessed techniques. Nevertheless, successful applications coming from other nature-inspired metaheuristics, e.g., ant algorithms, have also shown their applicability in dynamic optimization problems, but received a limited attention until now. Different from perturbative techniques, ant a

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Elastic Registration of Brain Cine-MRI Sequences Using MLSDO Dynamic Optimization Algorithm, of a brain cine-MR imaging. In this method, an elastic registration process is applied to a 2D+t cine-MRI sequence of a region of interest (i.e. lamina terminalis). This registration process consists in optimizing an objective function that can be considered as dynamic. Thus, a dynamic optimization

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Artificial Immune System for Solving Dynamic Constrained Optimization Problems,roach is called Dynamic Constrained T-Cell (DCTC) and it is an adaptation of an existing algorithm, which was originally designed to solve static constrained problems. Here, this approach is extended to deal with problems which change over time and whose solutions are subject to constraints. Our pro

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Low-Level Hybridization of Scatter Search and Particle Filter for Dynamic TSP Solving,F combines sequential estimation and combinatorial optimization methods to efficiently address dynamic optimization problems. SSPF obtains high quality solutions at each time step by taking advantage of the best solutions obtained in the previous ones. To demonstrate the performance of the proposed

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Insect Swarm Algorithms for Dynamic MAX-SAT Problems,limited number of theoretical and real-world problems come as instances of SAT or MAX-SAT, many combinatorial problems can be encoded into them. This puts the study of MAX-SAT and the development of adequate algorithms to address it in an important position in the field of computer science. Among th

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Dynamic Time-Linkage Evolutionary Optimization: Definitions and Potential Solutions,n influence how the problems might change in the future. Although DTPs are very common in real-world applications (e.g. online scheduling, online vehicle routing, and online optimal control problems), they have received very little attention from the evolutionary dynamic optimization (EDO) research

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