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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Esam Taha Yassen
University of Anbar
Computer Science & IT Engineering

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A self-adaptation algorithm for quay crane scheduling at a container terminal Esam Taha Yassen; Masri Ayob; Alaa Abdalqahar Jihad; Mohd Zakree Ahmad Nazri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 4: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i4.pp919-929

Abstract

Quay cranes scheduling at container terminals is a fertile area of study that is attracting researchers as well as practitioners in different parts of the world, especially in OR and artificial intelligence (AI). This process efficiency may affect the accomplishment and the competitive merits. As such, four local search algorithms (LSs) are utilized in the current work. These are hill climbing (HC), simulated annealing (SA), tabu search (TS), and iterated local search (ILS). The results obtained demonstrated that none of these LSs succeeded to achieve good results on all instances. This is because different QCSP instances have different characteristics with NP-hardness nature. Therefore, it is difficult to define which LS can yield the best outcomes for all instances. Consequently, appropriate LS selection should be governed by the type of problem and search status. The current work proposes to achieve this, the self-adaptation heuristic (self-H). The self-H is composed of two separate stages: The upper (LS-controller) and the lower (QCSP-solver). The LS-controller embeds an adaptive selection mechanism to adaptively select which LS is to be adopted by the QCSP-solver to solve the given problem. The results revealed that the self-H outperformed others as it attained better results over most instances and competitive results.
Lion optimization algorithm for team orienteering problem with time window Esam Taha Yassen; Alaa Abdulkhar Jihad; Sudad H. Abed
Indonesian Journal of Electrical Engineering and Computer Science Vol 21, No 1: January 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v21.i1.pp538-545

Abstract

Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.
Co-Authors Alaa Abdalqahar Jihad Alaa Abdulkhar Jihad Masri Ayob Mohd Zakree Ahmad Nazri Sudad H. Abed