Particle swarm optimization method in multiobjective problems

Parsopoulos, Konstantinos E.; Vrahatis, Michael N.

doi:10.1145/508791.508907

Cited by 436 publications

(221 citation statements)

References 13 publications

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“…However, some variants of other techniques have also been taken into account: SA (MOSA as proposed by Ulungu et al (1999) and PASA as developed by Suresh and Mohanasundaram (2004), PSO (Parsopoulos and Vrahatis (2002b) and ACO (Iredi et al (2001). Those variants have been adapted to solve multiobjective optimization problems.…”

Section: C18 Artificial Immune Systems (Ais)mentioning

confidence: 99%

Metaheuristic optimization frameworks: a survey and benchmarking

et al. 2011

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This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.

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Section: C18 Artificial Immune Systems (Ais)mentioning

confidence: 99%

Metaheuristic optimization frameworks: a survey and benchmarking

et al. 2011

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“…2 and 3, we know that the minimization of p j belongs to a constrained nonlinear optimization problem. The most common approach for solving constrained optimization problems is the use of a penalty function [19], which is generally defined as Table 3 Optimal production time per unit piece r(i,k) (min) without T l for various combinations of the total stocks and the initial work diameters The constrained problem then is transformed to an unconstrained one, by penalizing the constraints and building a single objective function. The PSO technique, a stochastic global optimization method which is inspired from simulation of collaborative behavior, is adopted to solve the unconstrained optimization problem.…”

Section: Proposed Methodologymentioning

confidence: 99%

Dynamic optimization of multipass turning of a flexible workpiece considering the effect of cutting sequence

Zhang

Jing

et al. 2015

Int J Adv Manuf Technol

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In multipass turning of flexible workpieces, the selection of the sequence of cutting passes is as important as the selection of machining parameters (depth of cut, feed rate, and cutting speed). This is because the varying workpiece diameter has an effect on the optimization objective function and constraints. In general, there should be a total of five decision variables in multipass turning optimization problems, that is, the three fundamental machining parameters, the number of cutting passes, and the sequence of cutting passes. However, this issue has been paid little attention in literature. This study presents an effective method for optimizing the cutting sequence and the machining parameters to suppress the deformation and chatter during turning a flexible cantilever tube. The mathematical optimization model distinct from the simplified model used in many previous studies is formulated, where the deformation and chatter constraints are intensively deduced from the dynamic analysis. The optimization problem is solved in two phases. The first phase is to determine the minimum production time for each cutting pass for the predefined depths of cut. A particle swarm optimization is employed to accomplish this step. After that, a dynamic programming technique is adopted to achieve the optimal sequential subdivision of the total depth of cut. A computational experiment elaborates the methodology. The results demonstrate that the proposed method is of generality and more efficient in comparison with other algorithms.

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“…Consequently it is not easy to decide x pb i and x gb which are for considering the information on the best positions of some particles. Since the first MOPSO was proposed by Moore and Chapman [19], several MOPSO algorithms [2,12,14,17,20,21,24,25,30,31] have been developed.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Generation of Pareto optimal solutions using generalized DEA and PSO

2015

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Meta-heuristic methods such as particle swarm optimization and genetic algorithms have been applied in solving multi-objective optimization problems, and have been observed to be useful for generating a good approximation of Pareto optimal solutions. This paper suggests a multi-objective particle swarm optimization (MOPSO) utilizing generalized data envelopment analysis (GDEA) in order to decide adaptively parameters of MOPSO as well as to improve the convergence and the diversity in the search of solutions. In addition, the effectiveness of the proposed method using GDEA will be investigated by comparison with conventional methods through several numerical examples.

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Particle swarm optimization method in multiobjective problems

Cited by 436 publications

References 13 publications

Metaheuristic optimization frameworks: a survey and benchmarking

Metaheuristic optimization frameworks: a survey and benchmarking

Dynamic optimization of multipass turning of a flexible workpiece considering the effect of cutting sequence

Generation of Pareto optimal solutions using generalized DEA and PSO

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