A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times

Zhou, Shengchao; Xing, Lining; 鄭, 旭光; Du, Ni; Wang, Ling; Zhang, Qingfu

doi:10.1109/tcyb.2019.2939219

Cited by 181 publications

(89 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) e individual energy of sparrow population depends on individual fitness evaluation, and the individual energy of discoverer is higher than that of discovers. (2) Once the scouters in the sparrow population find the threat of the external environment, they begin to send out an alarm signal. When the alert value is greater than the security threshold, the discoverers direct the population to the security zone.…”

Section: Sparrow Search Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Improved Sparrow Search Algorithm Based on Iterative Local Search

Yan

Yang

Zhu

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

show abstract

Section: Sparrow Search Algorithmmentioning

confidence: 99%

“…With the continuous emergence of various optimization problems, various algorithms and improved algorithms are emerging [1][2][3][4]. e emergence of swarm intelligence algorithm provides new ideas for solving various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Improved Sparrow Search Algorithm Based on Iterative Local Search

Yan

Yang

Zhu

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm was utilized in protein structure prediction and outperformed the competitors. Authors in [37] proposed a self-adaptive DE algorithm for addressing the batch-processing machine scheduling problem, in which the mutation operators and control parameter values are adaptively adjusted. In [22], a bi-objective elite DE was designed to optimize the multivalued logic networks.…”

Section: Related Workmentioning

confidence: 99%

Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation

Cao

Wang

et al. 2021

Data Sci. Eng.

View full text Add to dashboard Cite

By breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.

show abstract

“…Many metaheuristic based solution techniques have been applied for solving the workforce scheduling problems so far. ey include artificial bee colony algorithm [21], particle swarm optimization [22], migrating birds optimization [23], genetic algorithm (GA) [24], an adaptive multiple crossover GA [25], and a modified differential evolution (DE) algorithm ( [26][27][28][29]). A tabu-search hyperheuristics solution for nurse scheduling problem is presented in Burke et al's work [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperheuristic Based Migrating Birds Optimization Algorithm for a Fairness Oriented Shift Scheduling Problem

Alp

Alkaya

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The purpose of this paper is twofold. First, it introduces a new hybrid computational intelligence algorithm to the optimization community. This novel hybrid algorithm has hyperheuristic (HH) neighborhood search movements embedded into a recently introduced migrating birds optimization (MBO) algorithm. Therefore, it is called HHMBO. Second, it gives the necessary mathematical model for a shift scheduling problem of a manufacturing company defined by including the fairness perspective, which is typically ignored especially in manufacturing industry. Therefore, we call this complex optimization problem fairness oriented integrated shift scheduling problem (FOSSP). HHMBO is applied on FOSSP and is compared with the well-known simulated annealing, hyperheuristics, and classical MBO algorithms through extended computational experiments on several synthetic datasets. Experiments demonstrate that the new hybrid computational intelligence algorithm is promising especially for large sized instances of the specific problem defined here. HHMBO has a high exploration capability and is a promising technique for all optimization problems. To justify this assertion, we applied HHMBO to the well-known quadratic assignment problem (QAP) instances from the QAPLIB. HHMBO was up to 14.6% better than MBO on converging to the best known solutions for QAP benchmark instances with different densities. We believe that the novel hybrid method and the fairness oriented model presented in this study will give new insights to the decision-makers in the industry as well as to the researchers from several disciplines.

show abstract

A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times

Cited by 181 publications

References 58 publications

Improved Sparrow Search Algorithm Based on Iterative Local Search

Improved Sparrow Search Algorithm Based on Iterative Local Search

Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation

Hyperheuristic Based Migrating Birds Optimization Algorithm for a Fairness Oriented Shift Scheduling Problem

Contact Info

Product

Resources

About