Software performance optimization based on constrained GSA

Amoozegar, Maryam; Nezamabadi‐pour, Hossein

doi:10.1109/aisp.2012.6313732

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…There has been little contribution to the discussion of constrained optimization using GSA. A simple implementation is by the use of a penalty function, as Amoozegar and Nezamabadi-Pour [32] did. In general, however, penalty functions are not ideal as they distort the underlying search space and also introduce the problem of selecting an appropriate penalty term, which is highly case dependent.…”

Section: Suitable Constraint Handling Methodsmentioning

confidence: 98%

Analysis of constraint handling methods for the gravitational search algorithm

Poole¹,

Allen

Rendall³

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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The gravitational search algorithm (GSA) is a recent addition to the family of global optimization algorithms based on phenomena found in nature, specifically the gravitational attractive force between two bodies of mass. However, like almost all global search algorithms of this type, GSA has no direct method of handling a constrained optimization problem. There has been much attention to constraint handling using other agent based systems, though the mechanics of GSA make the application of many of these difficult. This paper has therefore analysed constraint handling methods for use with GSA and compared the performance of simple to implement methods (penalties and feasible directions) with a novel separation-sub-swarm (3S) approach, and found that feasible direction methods ideally need at least one initially feasible particle, and that the novel 3S approach is highly effective for solving constrained optimization problems using GSA outperforming the other approaches tested.

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Section: Suitable Constraint Handling Methodsmentioning

confidence: 98%

Analysis of constraint handling methods for the gravitational search algorithm

Poole¹,

Allen

Rendall³

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

show abstract

“…There has been little contribution to the discussion of constrained optimization using GSA. A simple implementation is by the use of a penalty function, as Amoozegar and Nezamabadi-Pour [35] did. In general, however, penalty functions are not ideal as they distort the underlying search space and also introduce the problem of selecting an appropriate penalty term, which is highly case dependent.…”

Section: Proposed Constrained Optimization Frameworkmentioning

confidence: 98%

Constraint handling in agent-based optimization by independent sub-swarms

Poole

Allen

Rendall

2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Agent-based optimization algorithms are an effective means of solving global optimization problems with design spaces containing multiple local minima, however, modifications have to be made to such algorithms to be able to solve constrained optimization problems. The gravitational search algorithm (GSA) is an efficient and effective agentbased method, however, the idea of global transfer of data that is key to the algorithm's success prohibits coupling of many state-of-the-art methods for handling constraints. Hence, a robust method, called separation-sub-swarm (3S) has been developed specifically for use with GSA by exploiting but also accommodating the global transfer of data that occurs in GSA, however it can also act as an entirely black-box module so is generally applicable. This newly developed 3S method has been shown to be efficient and effective at optimizing a suite of constrained analytical test functions using GSA.

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“…By inserting the (18) and (19) in (23), the field that based on the optimization parameters are defined as:…”

Section: Pseudo Code Of Igsamentioning

confidence: 99%

“…GSA is a novel technique that depends on the second law of motion and newton law of gravity. This algorithm emanates under the populationbased algorithm having agents of different masses [3] and has been applied to the number of the applications including power engineering [4][5][6][7], image processing [8][9][10], communication [11][12][13], controls [14][15][16], biology [17,18], and computer science [19]. Every agent in GSA is simulated as a matter and problem search space as the universe where each agent subject to the gravitational force.…”

Section: Introductionmentioning

confidence: 99%

An improved gravitational search algorithm for solving an electromagnetic design problem

Khan

Ling

2020

J Comput Electron

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Gravitational Search Algorithm (GSA) is a novel optimization technique that depends upon the law of motion and law of gravity of masses interaction between the agents. GSA has shown outstanding performance but has a drawback of having a slow exploitation process due to the fitness function influence on masses of the agents. As a result, after each iteration, the masses are getting heavier and restricts the movement. For that reason, masses cancel out the gravitational forces on each other and restricting them from quickly exploiting the optimum. To overcome this limitation, an improved GSA (IGSA) with a modified exploitation strategy is proposed in this paper. The primary aim of modification is to enhance the proficiency of the algorithm in terms of faster convergence and avoid premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. Simulation results confirmed that the proposed method has outstanding results in solving the Loney's Solenoid design problem and solution stability is much better as compared to the Standard gravitational search algorithm and various other state-of-the-art techniques previously applied for solving this problem.

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Software performance optimization based on constrained GSA

Cited by 9 publications

References 14 publications

Analysis of constraint handling methods for the gravitational search algorithm

Analysis of constraint handling methods for the gravitational search algorithm

Constraint handling in agent-based optimization by independent sub-swarms

An improved gravitational search algorithm for solving an electromagnetic design problem

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