Clustered genetic algorithm to solve multidimensional knapsack problem

Gupta, Indresh Kumar; Choubey, Abha; Choubey, Siddhartha

doi:10.1109/icccnt.2017.8204118

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…In [3] introduced a novel binary biogeography-based optimization algorithm for the knapsack problem, the result from [3] show that the new method is effective and efficient. In [5] implement a fast and efficient genetic algorithm to solve 0-1 knapsack problem feasibility and effectively. In [10] presents a discrete artificial bee colony for multiple knapsack problem, the result from that the presented method has enhanced convergence speed and quality than other evolutionary algorithms.…”

Section: -Related Workmentioning

confidence: 99%

“…given (C > 0), Wi > 0,Pi > 0, 1 ≤ i ≤ n, the zero-one knapsack problem can be represented by a vector of binary values X1,X2 …… Xn, Where Xi = 0 or 1 (1 ≤ i ≤ n). satisfy the constraint in equation 1 is the goal from finding a vector [5].…”

Section: -Description To Solve Knapsack Problem By Using Abc Algorithmmentioning

confidence: 99%

“…Many methods such as: branch and bound, cutting planes, and dynamic programming etc., and nature inspired algorithm have been proposed to solve the knapsack problem [3,4]. Lately, evolutionary algorithms such as genetic algorithm (GA) [5,6], particle swarm optimization algorithm (PSO) [7], ant colony optimization algorithm (ACO) [8] and artificial bee colony algorithm (ABC) [4] efficiently have been developed for solving knapsack problem and reaches to the best solution.…”

Section: -Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

Hasoon¹

2023

Tikrit J. Pure Sci.

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One of the combinatorial optimization problems is Knapsack problem, which aims to maximize the benefit of objects whose weight not exceeding the capacity of knapsack. This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined costs, this plan is one of the applications of the financial field. The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm

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Section: -Related Workmentioning

confidence: 99%

Section: -Description To Solve Knapsack Problem By Using Abc Algorithmmentioning

confidence: 99%

Section: -Introductionmentioning

confidence: 99%

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Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

Hasoon¹

2023

Tikrit J. Pure Sci.

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“…They have tested the performance of both versions of their selection process on seven unconstrained continuous optimization problems. Gupta et al [16] have elaborated a crossover operator based on k-means clustering technique. Their crossover operator uses the similarity concept between individuals in the genotypic search space of 0/1 MKP.…”

Section: Literature Reviewmentioning

confidence: 99%

On Solving 0/1 Multidimensional Knapsack Problem with a Genetic Algorithm Using a Selection Operator Based on K-Means Clustering Principle

Laabadi

Naïmi

Amri

et al. 2022

Foundations of Computing and Decision Sciences

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The growing need for profit maximization and cost minimization has made the optimization field very attractive to both researchers and practitioners. In fact, many authors were interested in this field and they have developed a large number of optimization algorithms to solve either academic or real-life problems. Among such algorithms, we cite a well-known metaheuristic called the genetic algorithm. This optimizer tool, as any algorithm, suffers from some drawbacks; like the problem of premature convergence. In this paper, we propose a new selection strategy hoping to avoid such a problem. The proposed selection operator is based on the principle of the k-means clustering method for the purpose of guiding the genetic algorithm to explore different regions of the search space. We have elaborated a genetic algorithm based on this new selection mechanism. We have then tested our algorithm on various data instances of the 0/1 multidimensional knapsack problem. The obtained results are encouraging when compared with those reached by other versions of genetic algorithms and those reached by an adapted version of the particle swarm optimization algorithm.

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“…Many papers have been published in the more general packing problems context, some examples of new approximation approaches are genetic algorithms [17], [19], [21], [31] and their biased versions [15], [16], divide and conquer algorithms (in which the solution space is partitioned and searched independently) [33], neuro-genetic approaches that mix neural networks and genetic algorithms [10], GRASP algorithms [27] and GRASP/Path relinking [1], Tabu search [4], [8] and other greedy randomized heuristics [7], [24].…”

Section: Previous Workmentioning

confidence: 99%

The Offline Group Seat Reservation Knapsack Problem With Profit on Seats

2019

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In this paper we present the Group Seat Reservation Knapsack Problem with Profit on Seat. This is an extension of the the Offline Group Seat Reservation Knapsack Problem. In this extension we introduce a profit evaluation dependant on not only the space occupied, but also on the individual profit brought by each reserved seat. An application of the new features introduced in the proposed extension is to influence the distribution of passengers, such as assigning seats near the carriage centre for long journeys, and close to the door for short journeys. Such distribution helps to reduce the excess of dwelling time on platform. We introduce a new GRASP based algorithm that solves the original problem and the newly proposed one. In the experimental section we show that such algorithm can be useful to provide a good feasible solution very rapidly, a desirable condition in many real world systems. Another application could be to use the algorithm solution as a startup for a successive branch and bound procedure when optimality is desired. We also add a new class of problem with five test instances that represent some challenging real-world scenarios that have not been considered before. Finally, we evaluate both the existing model, the newly proposed model, and analyse the pros and cons of the proposed algorithm.

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Clustered genetic algorithm to solve multidimensional knapsack problem

Cited by 4 publications

References 9 publications

Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

On Solving 0/1 Multidimensional Knapsack Problem with a Genetic Algorithm Using a Selection Operator Based on K-Means Clustering Principle

The Offline Group Seat Reservation Knapsack Problem With Profit on Seats

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