Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Htiouech, Skander; Alzaidi, Ameur

doi:10.5120/ijca2017915404

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…Additionally, Htiouech et al [65] used a surrogate constraint combined with an oscillation method to solve the MMKP. Subsequently, Htiouech and Alzaidi [66] divided the MMKP into small subproblems and used an agent-based approach to solve the reduced problem. Xia et al [67] proposed a first-level tabu search algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

2023

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The multiple-choice multidimensional knapsack problem (MMKP) is a well-known NP-hard problem that has many real-time applications. However, owing to its complexity, finding computationally efficient solutions for the MMKP remains a challenging task. In this study, we propose a Modified Artificial Bee Colony algorithm (MABC) to solve the MMKP. The MABC employs surrogate relaxation, Hamming distance, and a tabu list to enhance the local search process and exploit neighborhood information. We evaluated the performance of the MABC on standard benchmark instances and compared it with several stateof-the-art algorithms, including RLS, ALMMKP, ACO, PEGF-PERC, TIKS-TIKS 2 and D-QPSO. The experimental results reveal that MABC produces highly competitive solutions in terms of the best solutions found, achieving approximately 2% of the optimal solutions with trivial (milliseconds) CPU time. The Kruskal-Wallis test revealed that there was no statistically significant difference in the objective function values between the MABC algorithm and other state-of-the-art algorithms (H = 0.31506, p = 0.98882). However, for CPU efficiency, the test showed a statistically significant difference (H = 84.90850, p = 0), indicating that the MABC algorithm exhibited significantly better CPU efficiency (with shorter execution times) than the other algorithms did. Along with these findings, the ease of implementation of the algorithm and the small number of control parameters make our approach highly adaptive for large-scale real-time systems.INDEX TERMS Artificial bee colony algorithm, multiple-choice multidimensional knapsack problem, hamming distance, surrogate relaxation.

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Section: Literature Reviewmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

2023

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“…The authors formulated the underlying issue as a set-partitioning problem and then use the branch-and-price method to resolve it [20]. Moreover, investigations have been performed on the association of the multi-agent systems and KSP approach, The authors of [21] analyze multi-agent approach for solving multidimensional multi-choice KSP. They solve this by decomposing the problem into sub-problems.…”

Section: State Of the Artmentioning

confidence: 99%

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

Justo,

Gupta,

Umland

et al. 2023

Robotics

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Many service robots have to operate in a variety of different Service Event Areas (SEAs). In the case of the waste collection robot MARBLE (Mobile Autonomous Robot for Litter Emptying) every SEA has characteristics like varying area and number of litter bins, with different distances between litter bins and uncertain filling levels of litter bins. Global positions of litter bins and garbage drop-off positions from MARBLEs after reaching their maximum capacity are defined as task-performing waypoints. We provide boundary delimitation for characteristics that describe the SEA. The boundaries interpolate synergy between individual SEAs and the developed algorithms. This helps in determining which algorithm best suits an SEA, dependent on the characteristics. The developed route-planning methodologies are based on vehicle routing with simulated annealing (VRPSA) and knapsack problems (KSPs). VRPSA uses specific weighting based on route permutation operators, initial temperature, and the nearest neighbor approach. The KSP optimizes a route’s given capacity, in this case using smart litter bins (SLBs) information. The game-theory KSP algorithm with SLBs information and the KSP algorithm without SLBs information performs better on SEAs lower than 0.5 km2, and with fewer than 50 litter bins. When the standard deviation of the fill rate of litter bins is ≈10%, the KSP without SLB is preferred, and if the standard deviation is between 25 and 40%, then the game-theory KSP is selected. Finally, the vehicle routing problem outperforms in SEAs with an area of 0.5≤5 km2, 50–450 litter bins, and a fill rate of 10–40%.

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“…Lately, [160] which each item may belongs to several groups according to a predefined fuzzy membership value. They modeled the proposed problem as a bi-objective problem by using the epsilon-constraint method [162].…”

Section: Other Metaheuristic Approachesmentioning

confidence: 99%

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Laabadi¹,

Naimi²,

Amri³

et al. 2018

AJOR

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The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

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Smart Agents for the Multidimensional Multi-choice Knapsack Problem

Cited by 4 publications

References 25 publications

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

Modified Artificial Bee Colony Algorithm for Multiple-Choice Multidimensional Knapsack Problem

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

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