Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization

Ranjithan, S. Ranji; Chetan, S; Dakshina, Harish K.

doi:10.1007/3-540-44719-9_21

Cited by 29 publications

(22 citation statements)

References 16 publications

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“…As shown in Fig. 4, within comparable fitness evaluations, OEGADO outperformed OSGADO and NSGA-II in both distribution and spread [15]. OEGADO found the best minimum solution for f 1 with a value of 2.727 units.…”

Section: Resultsmentioning

confidence: 67%

“…The most recent ones are the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) [3], Strength Pareto Evolutionary Algorithm-II (SPEA-II) [16], Pareto Envelope based selection-II (PESA-II) [17]. Most of these approaches propose the use of a generational GA. Deb proposed an Elitist Steady State Multi-objective Evolutionary Algorithm (MOEA) [18] which attempts to maintain spread [15] while attempting to converge to the true Pareto-optimal front. This algorithm requires sorting of the population for every new solution formed thereby increasing its time complexity.…”

Section: Introductionmentioning

confidence: 99%

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Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. These methods are intended for solving real-world application problems that have many constraints and very small feasible regions. One method called Objective Exchange Genetic Algorithm for Design Optimization (OEGADO) runs several GAs concurrently with each GA optimizing one objective and exchanging information about its objective with the others. The other method called Objective Switching Genetic Algorithm for Design Optimization (OSGADO) runs each objective sequentially with a common population for all objectives. Empirical results in benchmark and engineering design domains are presented. A comparison between our methods and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) shows that our methods performed better than NSGA-II for difficult problems and found Pareto-optimal solutions in fewer objective evaluations. The results suggest that our methods are better applicable for solving real-world application problems wherein the objective computation time is large.

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Section: Resultsmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Chafekar

Jiang

Rasheed

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…It was introduced by Ranjithan [24]. A higher value of the spread metric indicates a better performance.…”

Section: Spreadmentioning

confidence: 99%

Optimization of Dynamic Data Types in Embedded Systems using DEVS/SOA-based Modeling and Simulation

Risco‐Martín¹,

Mittal²,

Atienza³

et al. 2008

Proceedings of the Third International ICST Conference on Scalable Information Systems

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New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of possible DDTs implementations. This results in the minimization of embedded design variables (memory accesses, power consumption and memory usage). Till date some effective heuristic algorithms have been developed in order to solve this problem, however unreported, as the problem is NPcomplete and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time but also to implement new algorithms. This research work provides a methodology to use Discrete Event Systems Specification (DEVS) to implement a parallel evolutionary algorithm within a Service Oriented Architecture (SOA), where parallelism improves the solutions found by the corresponding sequential algorithm. This algorithm provides better results when compared with other previously proposed procedures. In order to implement the parallelism the DEVS/SOA framework in utilized. Experimental results show how a novel parallel multi-objective genetic algorithm, which combines NSGA-II and SPEA2, allows designers to reach a larger number of solutions than previous approximations. This research also establishes DEVS/SOA as a platform for conducting complex distributed simulation experiments.

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“…It was introduced by Ranjithan [16]. A higher value of the spread metric indicates a better performance.…”

Section: Spread (D)mentioning

confidence: 99%

Design Flow of Dynamically-Allocated Data Types in Embedded Applications Based on Elitist Evolutionary Computation Optimization

Risco‐Martín

Atienza

Hidalgo

et al. 2008

2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools

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Constraint Method-Based Evolutionary Algorithm (CMEA) for Multiobjective Optimization

Cited by 29 publications

References 16 publications

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Constrained Multi-objective Optimization Using Steady State Genetic Algorithms

Optimization of Dynamic Data Types in Embedded Systems using DEVS/SOA-based Modeling and Simulation

Design Flow of Dynamically-Allocated Data Types in Embedded Applications Based on Elitist Evolutionary Computation Optimization

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