Constrained Optimization by ε Constrained Particle Swarm Optimizer with ε-level Control

Takahama, Tetsuyuki; Sakai, Setsuko

doi:10.1007/3-540-32391-0_105

Cited by 87 publications

(44 citation statements)

References 14 publications

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“…ε-constrainted processing was first proposed by Takahama in 2005 [15], and its basic principle is similar to the feasible solution constrained constraint, except that it relaxes the constraint violation through the function at the early stage of the search. The method will produce more infeasible solutions in the early stages of optimization, and will be updated until the number of iterations has been reached.…”

Section: Constraint Processing Methodsmentioning

confidence: 99%

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

Xianyong¹,

Quanyu²

2018

dtcse

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The optimization of the annual maintenance scheduling is a multidimensional and nonlinear integer optimization problem. In this paper, the annual maintenance scheduling model is established with the minimum cost of maintenance and the expected minimum power supply, and the pollination algorithm based on e constraint is proposed. By obtaining the Pareto optimal solutions and comparing the economic and reliability objectives of the optimization problem of the maintenance scheduling, the proposed algorithm can effectively improve the solution precision of the multiobjective optimization problem and the distribution of the optimal solution. Based on the annual inspection scheduling of 12 transmission lines in a region, this paper compares the proposed algorithm with the Differential Evolution Flower Pollination Algorithm with Time Variant Factor (TVDFPA) and the Simulated Annealing Pollination Algorithm (SFPA). Based on the result, ε-constrained algorithm is superior to the other two in performance and calculation time.

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Section: Constraint Processing Methodsmentioning

confidence: 99%

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

Xianyong¹,

Quanyu²

2018

dtcse

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“…These are the antithesis to penalty functions, where instead of combining the constraint violation and objective function, the two are optimized separately (Lu and Chen 2006;Liang and Suganthan 2006). These methods can generally be split into ''hard feasibility rules'' (binary tournament selection is an example of this Deb 2000) and ''soft feasibility rules'' (aconstrained (Takahama and Sakai 2005a) and -constrained (Takahama and Sakai 2005b) methods are examples). Care has to be taken with methods such as these that premature convergence is not obtained, or that many user-defined tuning parameters are not added.…”

Section: Differential Evolutionmentioning

confidence: 99%

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

2016

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A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.

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“…Takahama developed the α [113] and the ε [97,114] constrained methods, which consider the objective and constraints separately and allow all solutions are comparable with each other by replacing the ordinal comparisons with the α level and the ε level comparisons. With the lexicographic order of the ε level comparisons, the violation of constraints is more prior than fitness, the formula goes as follows:…”

Section: Lexicographic Separationmentioning

confidence: 99%

RhoGTPase Involvement in Breast Cancer Migration and Invasion

Simpson¹

2008

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Constrained Optimization by ε Constrained Particle Swarm Optimizer with ε-level Control

Cited by 87 publications

References 14 publications

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

Multi-objective Optimization of Transmission Line Annual Maintenance Scheduling Based on Flower Pollination Algorithm

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

RhoGTPase Involvement in Breast Cancer Migration and Invasion

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