Proceedings Article10.1109/CONFLUENCE.2017.7943137
Comparative study of metaheuristic algorithms using Knapsack Problem
Dikscha Sapra,Rashi Sharma,Arun Agarwal +2 more
- 01 Jan 2017
pp 134-137
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TL;DR: Tabu Search, Scatter Search and Local Search algorithms are compared taking execution time, solution quality and relative difference to best known quality, as metrics to compute the results of this NP-hard problem.
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Abstract: This paper aims to discuss and compare various metaheuristic algorithms applied to the “Knapsack Problem”. The Knapsack Problem is a combinatorial optimization maximization problem which requires to find the number of each weighted item to be included in a hypothetical knapsack, so the total weight is less than or equal to the required weight. To come to an optimized solution for such a problem, a variety of algorithms can possibly be used. In this paper, Tabu Search, Scatter Search and Local Search algorithms are compared taking execution time, solution quality and relative difference to best known quality, as metrics to compute the results of this NP-hard problem.
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Citations
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TL;DR: Some of the most popular nature-inspired optimization methods currently reported on the literature are analyzed, while also discussing their applications for solving real-world problems and their impact on the current literature.
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Smart City Response to Homelessness
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Hybrid quantum genetic algorithm with adaptive rotation angle for the 0-1 Knapsack problem in the IBM Qiskit simulator
Enrique Ballinas,Oscar Montiel +1 more
TL;DR: A Hybrid Quantum Genetic Algorithm with an Adaptive Rotation Angle (HQGAAA) for the 0-1 knapsack problem is presented and statistic tests demonstrated that this proposal is faster than the other quantum algorithms tested.
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Performance Comparison of Population-Based Quantum-Inspired Evolutionary Algorithms
Hasan Yetis,Mehmet Karakose +1 more
- 01 Nov 2019
TL;DR: While quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm are better at local search and more accurate results.
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Metaheuristics and Swarm Methods: A Discussion on Their Performance and Applications
Erik Cuevas,Fernando Fausto,Adrián González +2 more
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TL;DR: This chapter presents a discussion centered on several observable characteristics in nature-inspired methods and their influence on its overall performance, and presents a survey on some of the most important areas science and technology where nature- inspired algorithms have found applications.
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References
MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem
Maria João Alves,Marla Almeida +1 more
TL;DR: This paper presents a new multiobjective genetic algorithm based on the Tchebycheff scalarizing function, which aims to generate a good approximation of the nondominated solution set of the multiobjectives problem.
67
Metaheuristic Optimization Algorithms
Dimitris Souravlias,Konstantinos E. Parsopoulos,Ilias S. Kotsireas,Panos M. Pardalos +3 more
- 01 Jan 2021
TL;DR: In this paper, specific state-of-the-art metaheuristic optimization algorithms are outlined, which belong to the broad trajectory-based and population-based categories, and are particularly selected as they constitute the building blocks of algorithm portfolios presented in the forthcoming chapters.
2
A scatter search method for bi-criteria {0, 1}-knapsack problems
TL;DR: A scatter search (SS) based method for finding a good approximation of the non-dominated frontier for large size bi-criteria {0, 1}-knapsack instances is presented and the approach seems to be very efficient and the quality of the approximation is quite good.
A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
TL;DR: A comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life QAP and analyze their performance in terms of both runtime efficiency and solution quality shows that Genetic Al algorithm has a better solution quality.
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