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16th IEEE Int Conf on Dependable, Autonom and Secure Comp/16th IEEE Int Conf on Pervas Intelligence and Comp/4th IEEE Int Conf on Big Data Intelligence and Comp/3rd IEEE Cyber Sci and Technol Congress (DASC/PiCom/DataCom/CyberSciTech)

作者： Prince, Marc Lin, Fuhua Athabasca Univ Sch Comp & Informat Syst Athabasca AB Canada

ISBN: (纸本)9781538675182

This paper presents an agent-based simulation approach to evaluating hunting algorithm performance using Netlogo and the Belief, Desire, and Intent (BDI) architecture. Netlogo was customized to a number of two-dimensional scenarios with both hunting agents and prey agents interacting with each other and with the obstacles within the scenarios. The agents were developed with the flexibility to instantiate many types of hunters and prey. The differences in both the hunting agent and prey agents are with their skills (communications, perception, speed, etc.) and their cognitive abilities. To evaluate the viability of the approach, it was used to evaluate two hunting algorithms: the Lion Optimization algorithm (LOA) and the Grey Wolf Optimization (GWO) algorithm. The experimental results show that the LOA was more resilient to obstacles than was the GWO. In the presence of obstacles, the lionesses are more reliable in completing joint convergence onto the prey. While the wolves have a lower convergence rate, they display an ability to recover from the confusion caused by obstacles to finally close in on their prey. The research concludes that the NetLogo programming environment is successfully adapted to the BDI architecture and is very effective in structuring a large agent-based system.

关键词： hunting algorithm BDI agent agent-based modelling simulation

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Fire and manoeuvrer optimizer for flow shop scheduling problems

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EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 977-991页

作者： Belabid, Jabrane Univ Hassan II Casablanca Lab Math & Applicat FST POB 146 Mohammadia Morocco

The purpose of this paper is to introduce a new rich source of ideas and techniques that could be used to build new algorithms capable to solve numerous encountered optimisation problems in different fields of science and engineering. The art of war is one of the most rich disciplines in terms of already experimented strategies and tactics that can inspire researchers to design new powerful and efficient metaheuristics. The framework of the proposed method are inspired by the main war phases and contains seven components: initialization, intelligence, conception, suppression, advance, assault and exploitation. The basic fire and manoeuvre tactic is adopted in the suppression and advance phases. The proposed fire and manoeuvre algorithm (FMA) is a hybridization of a greedy algorithm with a multi-neighbouring search procedure. The developed algorithm has been employed to minimize makespan of the classical flow shop scheduling problem. A mathematical model is presented to describe the studied optimisation problem. Comparative experiments on Taillard's data set confirmed that the (FMA) results are more accurate than already published data. A comparison between the FMA and other popular nature-inspired algorithms has been conducted. It was revealed that the proposed metaheuristic outperforms the classical genetic algorithm, the migrating birds optimisation and the whale optimisation algorithm.

关键词： hunting algorithm Flow shop Mixed-integer linear programming Nature inspired algorithm

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