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International Journal of Applied Metaheuristic Computing
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ISSN (Print) 1947-8283 - ISSN (Online) 1947-8291
Published by IGI Global

[146 journals]

Comparative Analysis of Metaheuristic Algorithms for Procedural Race Track
Generation in Games
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  Abstract: Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, maps, stories, dialogues, quests, and characters. A wide variety of search algorithms can be applied to PCG problems; however, those most often used are variations of evolutionary algorithms. This study focuses on comparing three metaheuristic approaches applied to race track games, with the specific goal of evaluating the effectiveness of different algorithms in producing game content. To that end, a Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are applied to a game-level design task to attempt to identify any discernible differences in their performance and identify whether alternative algorithms offer desirable performance characteristics. The results of the study indicate that both the ABC and PSO approaches offer potential advantages to Genetic Algorithm implementation.
  Keywords: Artificial Intelligence; Computer Science & IT; Algorithms
  Citation: International Journal of Applied Metaheuristic Computing (IJAMC), Volume: 15, Issue: 1 (2024) Pages: 0-0
  PubDate: 2024-01-01T05:00:00Z
  DOI: 10.4018/IJAMC.350330
  Issue No: Vol. 15, No. 1 (2024)
An Artificial Bee Colony Algorithm for the Multidimensional Knapsack
Problem
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  Authors: Yaghini; Niusha, Valizadeh, Mir Yasin Seyed
  Pages: 1 - 23
  Abstract: The Multidimensional Knapsack Problem (MDKP) stands as a prominent challenge in combinatorial optimization, with diverse applications across various domains. The Artificial Bee Colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of bees. The aim of this paper is to develop an ABC with the goal of improving the solution quality in comparison to previous studies for the MDKP. In the proposed ABC algorithm, a heuristic method is presented to make employed bees. The roulette wheel and k-tournament methods are investigated for selecting employed bees by onlooker bees. For crossing over, two methods including one-point and uniform are studied. To tune the parameters, the Design of Experiment (DOE) method has been applied. The well-known benchmark test problems have been used to evaluate the proposed algorithm. The results show the absolute superiority of the solutions generated by the proposed algorithm in compared with the previous studies.
  Keywords: Artificial Intelligence; Computer Science & IT; Algorithms
  Citation: International Journal of Applied Metaheuristic Computing (IJAMC), Volume: 15, Issue: 1 (2024) Pages: 1-23
  PubDate: 2024-01-01T05:00:00Z
  DOI: 10.4018/IJAMC.350225
  Issue No: Vol. 15, No. 1 (2024)
Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA
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  Authors: Sabrina; Abid, Fatima, Debbat
  Pages: 1 - 20
  Abstract: The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.
  Keywords: Artificial Intelligence; Computer Science & IT; Algorithms
  Citation: International Journal of Applied Metaheuristic Computing (IJAMC), Volume: 15, Issue: 1 (2024) Pages: 1-20
  PubDate: 2024-01-01T05:00:00Z
  DOI: 10.4018/IJAMC.341804
  Issue No: Vol. 15, No. 1 (2024)

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