A Novel Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization for Solving Industrial Engineering Optimization Problems

Yao, Jinyan; Sha, Yongbai; Chen, Yanli; Zhao, Xiaoying

doi:10.3390/machines10080602

Cited by 5 publications

(4 citation statements)

References 63 publications

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“…Constraint (25) indicates that every part is processed. Constraints ( 26) and (27) indicate that all processing is non-pre-emptive and that processing cannot be interrupted once it starts. Constraint (28) specifies that the decision variables, including φ ij , y ihh , z ihgg and w ihgjj , are binary.…”

Section: Optimization Of Kittingmentioning

confidence: 99%

“…Many scholars have combined the posterior approach with meta-heuristic algorithms to solve multi-objective optimization problems. Meta-heuristic algorithms characterized by simple structure, flexibility, gradient-free information, easy implementation and a strong capability of circumventing local constraints can achieve a global optimum [27]. Currently, multi-objective meta-heuristics include particle swarm optimization [28], the immune genetic algorithm [29], the bat algorithm [30], etc.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Zhu

et al. 2022

Machines

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This paper studied a class of coupling problems of material assignment, part nesting, kit delivery and parallel machine scheduling. The aim of this paper was to solve the scheduling problem of metal structural part processing and welding assembly with eligibility constraints. A two-stage mixed-integer programming model was constructed. The eligibility constraints took into account the material type of parts and nesting. The objectives were to minimize the makespan, maximize material utilization and minimize the kit delivery metrics (kitting time and numbers of earliness and tardiness of kits). A hierarchical optimization approach was proposed. The scheduling model was solved by using the Gurobi solver in the first stage, and the results were used to constrain the second stage. The second stage of the scheduling model was solved using an improved multi-objective genetic algorithm. Due to the strong coupling relationships among the sorting of parts, the sorting of each profile and the sorting of each material, a hybrid encoding and decoding mode was designed for part sorting with eligibility constraints. Finally, the proposed scheduling approach was applied to actual production cases. The data showed that when the number of components exceeded 300 (the number of parts was about 1500), the material utilization reached 95%. Choosing a suitable number of machines, machine utilization reached 90%. The results demonstrated the effectiveness of the proposed scheduling model and algorithm.

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Section: Optimization Of Kittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Zhu

et al. 2022

Machines

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“…In recent years, optimization problems have gained significant attention in various fields, including engineering, economics, and computer science [1]. For engineering optimization problems, it is of great potential to pursue the optimal or the best solution to enhance diverse targets, such as production safety, efficiency, and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

Zhou,

Shi,

Wang

et al. 2024

Mathematics

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This paper introduces the election optimization algorithm (EOA), a meta-heuristic approach for engineering optimization problems. Inspired by the democratic electoral system, focusing on the presidential election, EOA emulates the complete election process to optimize solutions. By simulating the presidential election, EOA introduces a novel position-tracking strategy that expands the scope of effectively solvable problems, surpassing conventional human-based algorithms, specifically, the political optimizer. EOA incorporates explicit behaviors observed during elections, including the party nomination and presidential election. During the party nomination, the search space is broadened to avoid local optima by integrating diverse strategies and suggestions from within the party. In the presidential election, adequate population diversity is maintained in later stages through further campaigning between elite candidates elected within the party. To establish a benchmark for comparison, EOA is rigorously assessed against several renowned and widely recognized algorithms in the field of optimization. EOA demonstrates superior performance in terms of average values and standard deviations across the twenty-three standard test functions and CEC2019. Through rigorous statistical analysis using the Wilcoxon rank-sum test at a significance level of 0.05, experimental results indicate that EOA consistently delivers high-quality solutions compared to the other benchmark algorithms. Moreover, the practical applicability of EOA is assessed by solving six complex engineering design problems, demonstrating its effectiveness in real-world scenarios.

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“…For instance, WU Husheng [10] simulated the cooperative strategy of a wolf pack surrounding prey, but this algorithm tends to become trapped in local optima and struggles to escape. Yao's Harris-Hawk algorithm [23], which simulates the entire hunting process of hawks, is complex and computationally demanding. Faramarzi [24] proposed a marine predator algorithm that integrates multiple mechanisms to achieve improved global search capabilities, but a specific approach for addressing local optima is lacking.…”

Section: Introductionmentioning

confidence: 99%

Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

Liu,

Liao,

Shi

et al. 2024

Future Internet

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The objective of task allocation in unmanned systems is to complete tasks at minimal costs. However, the current algorithms employed for coordinating multiple unmanned systems in task allocation tasks frequently converge to local optima, thus impeding the identification of the best solutions. To address these challenges, this study builds upon the sheep flock optimization algorithm (SFOA) by preserving individuals eliminated during the iterative process within a prior knowledge set, which is continuously updated. During the reproduction phase of the algorithm, this prior knowledge is utilized to guide the generation of new individuals, preventing their rapid reconvergence to local optima. This approach aids in reducing the frequency at which the algorithm converges to local optima, continually steering the algorithm towards the global optimum and thereby enhancing the efficiency of task allocation. Finally, various task scenarios are presented to evaluate the performances of various algorithms. The results show that the algorithm proposed in this paper is more likely than other algorithms to escape from local optima and find the global optimum.

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A Novel Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization for Solving Industrial Engineering Optimization Problems

Cited by 5 publications

References 63 publications

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Multi-Objective Parallel Machine Scheduling with Eligibility Constraints for the Kitting of Metal Structural Parts

Election Optimizer Algorithm: A New Meta-Heuristic Optimization Algorithm for Solving Industrial Engineering Design Problems

Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm

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