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MOCell: A cellular genetic algorithm for multiobjective optimization
TLDR
This paper introduces a new cellular genetic algorithm called MOCell, characterized by using an external archive to store nondominated solutions and a feedback mechanism in which solutions from this archive randomly replace existing individuals in the population after each iteration.
View on Wiley
AbYSS: Adapting Scatter Search to Multiobjective Optimization
TLDR
The result is a hybrid metaheuristic algorithm called Archive-Based hYbrid Scatter Search (AbYSS), which follows the scatter search structure but uses mutation and crossover operators from evolutionary algorithms, which outperforms the other two algorithms as regards the diversity of the solutions.
View on IEEE
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
TLDR
This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors and concludes that dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy.
View on IEEE
Improving Classical and Decentralized Differential Evolution With New Mutation Operator and Population Topologies
TLDR
A new heterogeneous decentralized DE algorithm combining the two studied operators in the best performing studied population structure has been designed and evaluated and is shown to improve the previously obtained results, and outperform the compared state-of-the-art DEs.
View on IEEE
jMetal: a Java Framework for Developing Multi-Objective Optimization Metaheuristics
TLDR
jMetal provides a rich set of classes which can be used as the building blocks of multi-objective metaheuristics; thus, taking advantage of code-reusing, the algorithms share the same base components, such as implementations of genetic operators and density estimators, so making the fair comparison of different meta heuristics for MOPs possible.
Solving the Vehicle Routing Problem by Using Cellular Genetic Algorithms
TLDR
The conclusions are that cGAs with an added local search operator are able of always locating the optimum of the problem at low times and reasonable effort for the tested instances.
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A Study of the Combination of Variation Operators in the NSGA-II Algorithm
TLDR
Whether a more dynamic approach combining different operators with variable application rate along the search process allows to improve the static classical behavior of the NSGA-II algorithm is analyzed.
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Design Issues in a Multiobjective Cellular Genetic Algorithm
TLDR
This paper focuses here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two different replacement strategies, and evaluates the resulting algorithms using a benchmark of problems and compares the best of them against two state-of-the-art genetic algorithms for multiobjective optimization.
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Computing nine new best-so-far solutions for Capacitated VRP with a cellular Genetic Algorithm
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Efficient Batch Job Scheduling in Grids Using Cellular Memetic Algorithms
TLDR
This work exploits the capabilities of cellular memetic algorithms (cMAs), a kind of memetic algorithm with structured population, for obtaining efficient batch schedulers for grid systems, and the obtained results will be compared versus the state of the art.
View on IEEE
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