Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Liu, Sichen; Chen, Zong-Gan; Zhan, Zhi-Hui; Jeon, Sang-Woon; Zhang, Mengjie

doi:10.1109/tcyb.2021.3102642

Cited by 43 publications

(27 citation statements)

References 63 publications

(67 reference statements)

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“…Liu et al [33] designed a co-evolutionary particle swarm algorithm to improve convergence. Liu et al [34] designed a many-objective multi-population genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Sang

Tan

2022

Energies

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With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.

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“…Liu et al [33] designed a co-evolutionary particle swarm algorithm to improve convergence. Liu et al [34] designed a many-objective multi-population genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Sang

Tan

2022

Energies

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“…Bottleneck objective learning strategy, elitist learning strategy, and juncture learning strategy are applied simultaneously to improve the performance and efficiency of the algorithm. Liu et al [90] proposed a MPMO frameworkbased GA to efficiently solve five objectives job-shop scheduling problem. Archive sharing technique and archive update strategy were designed to guide the coevolution of all the populations and enhance the quality of the elite solutions.…”

Section: Mpmo-based Algorithmsmentioning

confidence: 99%

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

Chen

Zhao

Yuan

2022

Mobile Information Systems

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Due to the volatility and uncertainty of the financial market, investors often use the form of portfolio to actively manage their assets. Portfolio optimization (PO) is becoming more and more important for investors. However, PO is frequently a kind of NP-hard problem in the field of modern financial optimization, which has gradually attracted the attention and interest of researchers. Some efficient mathematical models were built to describe the return and risk of portfolio. A lot of precise and approximate fast algorithms are used to solve the established PO models. The fundamental purpose is to maximize the return and to minimize the risk of portfolio under certain constraints. In recent years, researchers not only limit the goal of PO to the balance between risk and return, but also pay attention to liquidity, environmental, social, and governance (ESG) controversy level, Sortino ratio, and other indicators. The number of PO targets and constraints is further extended. In the past two decades, swarm intelligence (SI) algorithms have been widely introduced to solve PO problems. SI algorithm is mainly inspired from the daily phenomena in nature or self-organization, self-adaptation, and self-learning of biological population. The existing research results show that SI algorithm has the characteristics of high efficiency and can obtain satisfactory solutions in solving PO problems. The recent advances on the classic portfolio optimization concepts, models, and the usual SI-based solving algorithms are presented. Finally, future potential research directions are presented.

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“…For example, vehicle routing problems often contain a lot of problem instances that are similar in terms of search space, problem characteristics, or optimal solutions [6]. Furthermore, by properly integrating knowledge learning [7] [8] and transfer methods [9] [10] with EC algorithms, such as genetic algorithm (GA) [11]- [14], particle swarm optimization (PSO) [15]- [18], and differential evolution (DE) [19]- [21], EMTO algorithms can solve complex real-world multitask applications efficiently. Therefore, EMTO has received increasing research attention in recent years [22]- [25].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

Zhan

Tan

et al. 2022

IEEE Trans. Evol. Computat.

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Abstract-Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge such as the high-quality solution from one task to other tasks to enhance the optimization ability, which however may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this paper proposes a meta-knowledge transfer-based differential evolution (MKTDE) algorithm by using a more general meta-knowledge transfer (MKT) method to solve MTOPs more efficiently. The meta-knowledge defined in this paper refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of "knowledge of knowledge", which denotes the knowledge of "how to solve problem via evolution" and "the feature of evolving high-quality solution". The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multi-source data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is a multiple populations for multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely-used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms.

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Many-Objective Job-Shop Scheduling: A Multiple Populations for Multiple Objectives-Based Genetic Algorithm Approach

Cited by 43 publications

References 63 publications

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Swarm Intelligence Algorithms for Portfolio Optimization Problems: Overview and Recent Advances

A Meta-Knowledge Transfer-Based Differential Evolution for Multitask Optimization

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