BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY Article Swipe

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YOU? · · 2015 · Open Access · · DOI: https://doi.org/10.47839/ijc.14.2.807

Multi Dimensional Knapsack problem is a widely studied NP hard problem requiring extensive processing to achieve optimality. Simulated Annealing (SA) unlike other is capable of providing fast solutions but at the cost of solution quality. This paper focuses on making SA robust in terms of solution quality while assuring faster convergence by incorporating effective fitness landscape parameters. For this it proposes to modify the ‘Acceptance Probability’ function of SA. The fitness landscape evaluation strategies are embedded to Acceptance Probability Function to identify the exploitation and exploration of the search space and analyze the behavior on the performance of SA. The basis of doing so is that SA in the process of reaching optimality ignores the association between the search space and fitness space and focuses only on the comparison of current solution with optimal solution on the basis of temperature settings at that point. The idea is implemented in two different ways i.e. by making use of Fitness Distance Correlation and Auto Correlation functions. The experiments are conducted to evaluate the resulting SA on the range of MKP instances available in the OR library.

Related Topics

Simulated Annealing

Computer Science

Boosting (Machine Learning)

Range (Aeronautics)

Mathematics

Artificial Intelligence

Concepts

Knapsack problem Simulated annealing Fitness landscape Mathematical optimization Computer science Fitness function Basis (linear algebra) Fitness approximation Boosting (machine learning) Quality (philosophy) Range (aeronautics) Convergence (economics) Mathematics Artificial intelligence Genetic algorithm Population Sociology Geometry Demography Materials science Epistemology Philosophy Economics Composite material Economic growth

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Type: article
Language: en
Landing Page: https://doi.org/10.47839/ijc.14.2.807
PDF: https://computingonline.net/computing/article/download/807/739
OA Status: diamond
Cited By: 2
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DOI: https://doi.org/10.47839/ijc.14.2.807

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Title: BOOSTING SIMULATED ANNEALING WITH FITNESS LANDSCAPE PARAMETERS FOR BETTER OPTIMALITY

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Language: en

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Publication year: 2015

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Publication date: 2015-06-30

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Authors: Sunanda Gupta, Sakshi Arora

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Landing page: https://doi.org/10.47839/ijc.14.2.807

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Concepts: Knapsack problem, Simulated annealing, Fitness landscape, Mathematical optimization, Computer science, Fitness function, Basis (linear algebra), Fitness approximation, Boosting (machine learning), Quality (philosophy), Range (aeronautics), Convergence (economics), Mathematics, Artificial intelligence, Genetic algorithm, Population, Sociology, Geometry, Demography, Materials science, Epistemology, Philosophy, Economics, Composite material, Economic growth

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Cited by: 2

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Citations by year (recent): 2025: 1, 2018: 1

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