Applied Metaheuristic Computing: 2nd Volume

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closed (31 May 2023)

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closed (31 August 2023)

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Topic Information

Dear Colleagues,

For decades, applied metaheuristic computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, and facility layout planning, among others. This is partly because classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, by contrast, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. The most commonly used AMC methods include:

• Ant colony optimization;
• Differential evolution;
• Evolutionary computation;
• Genetic algorithm;
• GRASP;
• Hyper-heuristics;
• Memetic algorithm;
• Particle swarm optimization;
• Scatter search;
• Simulated annealing;
• Tabu search;
• Variable neighborhood search.

I encourage the submission of your best papers within the topic of AMC.

Prof. Dr. Peng-Yeng Yin
Prof. Dr. Ray-I Chang
Prof. Dr. Jen-Chun Lee
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	15 Days	CHF 1600
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600
Symmetry symmetry	2.2	5.4	2009	16.8 Days	CHF 2400
AI ai	3.1	7.2	2020	17.6 Days	CHF 1600

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Published Papers (5 papers)

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29 pages, 5785 KiB  

Open AccessArticle

A Novel Evolutionary Algorithm: One-Dimensional Subspaces Optimization Algorithm (1D-SOA)

by Gabriela Berenice Díaz-Cortés and René Luna-García

Symmetry 2023, 15(10), 1873; https://doi.org/10.3390/sym15101873 - 5 Oct 2023

Cited by 1 | Viewed by 1079

Abstract

This paper introduces an evolutionary algorithm for n-dimensional single objective optimization problems: One-Dimensional Subspaces Optimization Algorithm (1D-SOA). The algorithm starts with an initial population in randomly selected positions. For each individual, a percentage of the total number of dimensions is selected, each dimension [...] Read more.

This paper introduces an evolutionary algorithm for n-dimensional single objective optimization problems: One-Dimensional Subspaces Optimization Algorithm (1D-SOA). The algorithm starts with an initial population in randomly selected positions. For each individual, a percentage of the total number of dimensions is selected, each dimension corresponding to a one-dimensional subspace. Later, it performs a symmetric search for the nearest local optima in all the selected one-dimensional subspaces (1D-S), for each individual at a time. The search stops if the new position does not improve the value of the objective function over all the selected 1D-S. The performance of the algorithm was compared against 11 algorithms and tested with 30 benchmark functions in 2 dimensions (D) and 30D. The proposed algorithm showed a better performance than all other studied algorithms for large dimensions. Full article

(This article belongs to the Topic Applied Metaheuristic Computing: 2nd Volume)

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28 pages, 2294 KiB  

Open AccessArticle

An Adaptive Jellyfish Search Algorithm for Packing Items with Conflict

by Walaa H. El-Ashmawi, Ahmad Salah, Mahmoud Bekhit, Guoqing Xiao, Khalil Al Ruqeishi and Ahmed Fathalla

Mathematics 2023, 11(14), 3219; https://doi.org/10.3390/math11143219 - 22 Jul 2023

Cited by 2 | Viewed by 1255

Abstract

The bin packing problem (BPP) is a classic combinatorial optimization problem with several variations. The BPP with conflicts (BPPCs) is not a well-investigated variation. In the BPPC, there are conditions that prevent packing some items together in the same bin. There are very [...] Read more.

The bin packing problem (BPP) is a classic combinatorial optimization problem with several variations. The BPP with conflicts (BPPCs) is not a well-investigated variation. In the BPPC, there are conditions that prevent packing some items together in the same bin. There are very limited efforts utilizing metaheuristic methods to address the BPPC. The current methods only pack the conflict items only and then start a new normal BPP for the non-conflict items; thus, there are two stages to address the BPPC. In this work, an adaption of the jellyfish metaheuristic has been proposed to solve the BPPC in one stage (i.e., packing the conflict and non-conflict items together) by defining the jellyfish operations in the context of the BPPC by proposing two solution representations. These representations frame the BPPC problem on two different levels: item-wise and bin-wise. In the item-wise solution representation, the adapted jellyfish metaheuristic updates the solutions through a set of item swaps without any preference for the bins. In the bin-wise solution representation, the metaheuristic method selects a set of bins, and then it performs the item swaps from these selected bins only. The proposed method was thoroughly benchmarked on a standard dataset and compared against the well-known PSO, Jaya, and heuristics. The obtained results revealed that the proposed methods outperformed the other comparison methods in terms of the number of bins and the average bin utilization. In addition, the proposed method achieved the lowest deviation rate from the lowest bound of the standard dataset relative to the other methods of comparison. Full article

(This article belongs to the Topic Applied Metaheuristic Computing: 2nd Volume)

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26 pages, 3620 KiB  

Open AccessArticle

Multi-Strategy Enhanced Dung Beetle Optimizer and Its Application in Three-Dimensional UAV Path Planning

by Qianwen Shen, Damin Zhang, Mingshan Xie and Qing He

Symmetry 2023, 15(7), 1432; https://doi.org/10.3390/sym15071432 - 17 Jul 2023

Cited by 24 | Viewed by 3830

Abstract

Path planning is a challenging, computationally complex optimization task in high-dimensional scenarios. The metaheuristic algorithm provides an excellent solution to this problem. The dung beetle optimizer (DBO) is a recently developed metaheuristic algorithm inspired by the biological behavior of dung beetles. However, it [...] Read more.

Path planning is a challenging, computationally complex optimization task in high-dimensional scenarios. The metaheuristic algorithm provides an excellent solution to this problem. The dung beetle optimizer (DBO) is a recently developed metaheuristic algorithm inspired by the biological behavior of dung beetles. However, it still has the drawbacks of poor global search ability and being prone to falling into local optima. This paper presents a multi-strategy enhanced dung beetle optimizer (MDBO) for the three-dimensional path planning of an unmanned aerial vehicle (UAV). First, we used the Beta distribution to dynamically generate reflection solutions to explore more search space and allow particles to jump out of the local optima. Second, the Levy distribution was introduced to handle out-of-bounds particles. Third, two different cross operators were used to improve the updating stage of thief beetles. This strategy accelerates convergence and balances exploration and development capabilities. Furthermore, the MDBO was proven to be effective by comparing seven state-of-the-art algorithms on 12 benchmark functions, the Wilcoxon rank sum test, and the CEC 2021 test suite. In addition, the time complexity of the algorithm was also analyzed. Finally, the performance of the MDBO in path planning was verified in the three-dimensional path planning of UAVs in oil and gas plants. In the most challenging task scenario, the MDBO successfully searched for feasible paths with the mean and standard deviation of the objective function as low as 97.3 and 32.8, which were reduced by 39.7 and 14, respectively, compared to the original DBO. The results demonstrate that the proposed MDBO had improved optimization accuracy and stability and could better find a safe and optimal path in most scenarios than the other metaheuristics. Full article

(This article belongs to the Topic Applied Metaheuristic Computing: 2nd Volume)

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23 pages, 1361 KiB  

Open AccessArticle

A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

by Jesus Hernandez-Barragan, Gabriel Martinez-Soltero, Jorge D. Rios, Carlos Lopez-Franco and Alma Y. Alanis

Mathematics 2022, 10(21), 4135; https://doi.org/10.3390/math10214135 - 5 Nov 2022

Cited by 4 | Viewed by 2227

Abstract

This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The [...] Read more.

This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The kinematic analysis does not require using any Jacobian matrix nor its estimation; for this reason, the proposed approach does not suffer from singularities, which is a common problem with conventional inverse kinematics algorithms. Moreover, the proposed method solves cooperative manipulation tasks, especially in the case of coordinated manipulation. Simulation and real-world experiments were performed to verify the proposal’s effectiveness under coordinated inverse kinematics and trajectory tracking tasks. The experimental setup considered a mobile dual-arm system based on the KUKA^® Youbot^® robot. The solution of the inverse kinematics showed precise and accurate results. Although the proposed approach focuses on coordinated manipulation, it can be implemented to solve non-coordinated tasks. Full article

(This article belongs to the Topic Applied Metaheuristic Computing: 2nd Volume)

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25 pages, 2773 KiB  

Open AccessArticle

A Hybrid Search Using Genetic Algorithms and Random-Restart Hill-Climbing for Flexible Job Shop Scheduling Instances with High Flexibility

by Nayeli Jazmin Escamilla-Serna, Juan Carlos Seck-Tuoh-Mora, Joselito Medina-Marin, Irving Barragan-Vite and José Ramón Corona-Armenta

Appl. Sci. 2022, 12(16), 8050; https://doi.org/10.3390/app12168050 - 11 Aug 2022

Cited by 9 | Viewed by 3052

Abstract

This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high [...] Read more.

This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high number of machines). In particular, different GA crossover and simple mutation operators are used with a cellular automata (CA)-inspired neighborhood to perform global search. This method is refined with a local search based on RRHC, making computational implementation easy. The novel point is obtained by applying the CA-type neighborhood and hybridizing the aforementioned two techniques in the GA-RRHC, which is simple to understand and implement. The GA-RRHC is tested by taking four banks of experiments widely used in the literature and comparing their results with six recent algorithms using relative percentage deviation (RPD) and Friedman tests. The experiments demonstrate that the GA-RRHC is a competitive method compared with other recent algorithms for instances of the FJSSP with high flexibility. The GA-RRHC was implemented in Matlab and is available on Github. Full article

(This article belongs to the Topic Applied Metaheuristic Computing: 2nd Volume)

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