A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

An important implementation issue in the design of hybrid evolutionary multiobjective optimization algorithms with local search (i.e., multiobjective memetic algorithms) is how to strike a balance between local search and global search. If local search is applied to all individuals at every generation, almost all computation time is spent by local search. As a result, global search ability of memetic algorithms is not well utilized. We can use three ideas for decreasing the computation load of local search. One idea is to apply local search to only a small number of individuals. This idea can be implemented by introducing a local search probability, which is used to choose only a small number of initial solutions for local search from the current population. Another idea is a periodical (i.e., intermittent) use of local search. This idea can be implemented by introducing a local search interval (e.g., every 10 generations), which is used to specify when local search is applied. The other idea is an early termination of local search. Local search for each initial solution is terminated after a small number of neighbors are examined. This idea can be implemented by introducing a local search length, which is the number of examined neighbors in a series of iterated local search from a single initial solution. In this paper, we discuss the use of these three ideas to strike a local-global search balance. Through computational experiments on a two-objective 500-item knapsack problem, we compare various settings of local search such as short local search from all individuals at every generation, long local search from only a few individuals at every generation, and periodical long local search from all individuals. Global search in this paper means genetic search by crossover and mutation in multiobjective memetic algorithms.

A decomposition-based multi-objective particle swarm optimization algorithm with a local search strategy for key quality characteristic identification in production processes

A well-planned schedule is essential to any organization’s growth. Thus, it is important for the literature to cover a more comprehensive range of scheduling problems. In this paper, energy-efficient hybrid flow shop (EEHFS) scheduling problems are considered. Researchers have developed several techniques to deal with EEHFS scheduling problems. Also, researchers have recently proposed several metaheuristics. Honey Badger Algorithm (HBA) is one of the most recent algorithms proposed to solve various optimization problems. The objective of the present work is to solve EEHFS scheduling problems using the Hybrid Honey Badger Algorithm (HHBA) to reduce the makespan (Cmax) and total energy cost (TEC). In the HHBA, a constructive heuristic known as the NEH heuristic was incorporated with the Honey Badger Algorithm. The suggested algorithm’s performance was verified using an actual industrial scheduling problem. The company’s results are compared with those of the HHBA. The HHBA could potentially result in an 8% decrease in total energy cost. Then, the proposed algorithm was applied to solve 54 random benchmark problems. The results of the proposed HHBA were compared with the FIFO dispatching rule, the NEH heuristic, and other metaheuristics such as the simulated annealing (SA) algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, Honey Badger Algorithm, and the Ant Colony Optimization (ACO) algorithms addressed in the literature. Average percentage deviation (APD) was the performance measure used to compare different algorithms. The APD of the proposed HHBA was zero. This indicates that the proposed HHBA is more effective in solving EEHFS scheduling problems.

This paper investigates the multistage hybrid flow shop (HFS) scheduling problems using the new bat algorithm. A HFS is the generalisation of flowshop with multiple machines. HFS is one of the important scheduling problems that represent many industries like iron and steel, chemical, textile and ceramic industries. The HFS scheduling problems have been proved to be NP-hard. A recently developed bat meta-heuristic algorithm is proposed to solve the HFS problems. The proposed algorithm is validated with a well-chosen set of benchmark problems in the literature. Computational results indicate that the proposed bat algorithm is more efficient than the genetic algorithm and particle swarm optimisation.

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

The particle swarm optimizer (PSO) is a swarm intelligence based heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of tranditioal PSO, this paper presents a new PSO variant based on local stochastic search strategy (LSSPSO) for performance enhancement. This is inspired by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO adopts a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of tranditioal PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.Keywordsparticle swarm optimizationcontinuous optimizationlocal stochastic search

The success of local search techniques in the solution of combinatorial optimization problems has motivated their incorporation into multi-objective evolutionary algorithms, giving rise to the so-called multi-objective memetic algorithms (MOMAs). The main advantage for adopting this sort of hybridization is to speed up convergence to the Pareto front. However, the use of MOMAs introduces new issues, such as how to select the solutions to which the local search will be applied and for how long to run the local search engine (the use of such a local search engine has an extra computational cost). Here, we propose a new MOMA which switches between a hypervolume-based global optimizer and an IGD+-based local search engine. Our proposed local search engine adopts a novel clustering technique based on the IGD+ indicator for splitting the objective space into sub-regions. Since both computing the hypervolume and applying a local search engine are very costly procedures, we propose a GPU-based parallelization of our algorithm. Our preliminary results indicate that our MOMA is able to converge faster than SMS-EMOA to the true Pareto front of multi-objective problems having different degrees of difficulty.

This paper presents a multiobjective memetic algorithm based on adaptive local search chains (MMA-ALSC) for vehicle routing problem with time window (VRPTW) which is an important research area in logistics. As shown in most previous studies, VRPTW is essentially a multiobjective optimization problem and can be solved effectively by the multiobjective algorithms with various local search operators. We have observed, however, that the promising solutions obtained during the process of evolution are not fully utilized to guide the search together with different local search operators. This will lead to the discontinuous and insufficient search in the regions showing promise. To alleviate this drawback, MMA-ALSC is proposed and characterized by combining a multi-directional local search strategy (MD-LS) with an enhanced local search chain technique (eLS-Chain). In MMA-ALSC, on the one hand, with MD-LS, different local search operators are designed to perform the search towards multiple directions with distinct problem-specific knowledge of multiobjective VRPTW (MOVRPTW). On the other hand, with eLS-Chain, the promising solutions obtained during the process of evolution are adaptively selected for the subsequent local search operators. In this way, MMA-ALSC can not only effectively explore the search space in multiple directions, but also fully exploit the promising solutions in a chain-based way. Experimental results on two suites of benchmark instances have demonstrated the competitive performance of MMA-ALSC when compared with other representative algorithms.

End-of-life products recycling can reduce the waste of resources, and disassembly line scheduling planning can effectively improve the recycling efficiency and reduce the pollution of the environment. This work addresses a bi-objective disassembly line scheduling problem with considering time interference between tasks. The weighted sum of the cycle time and hazard coefficients is optimized. First, a mathematical model of the disassembly line scheduling problem is established under the constraints of priority and time interference relationships. Second, four meta-heuristics are improved to solve the concerned problems, including particle swarm optimization, artificial bee colony, genetic algorithm and variable neighborhood search. Ten objective-oriented local search operations are designed for improving meta-heuristics’ performance. A reinforcement learning algorithm, Sarsa, is employed to guide task assignment among workstations and local search selection during iterations, respectively. Finally, experiments are carried out for 10 instances with different scales. The effectiveness of the improving strategies is verified; the meta-heuristics combined with Sarsa based task assignment and local search strategies has better robustness and stability than the classical ones. Comparisons and discussions show that the particle swarm optimization with improved strategies outperforms other algorithms.

Particle swarm optimization (PSO) is an evolutionary algorithm that is well known for its simplicity and effectiveness. It usually has strong global search capability but has the drawback of being easily trapped by local optima. A scaling mutation strategy and an elitist learning strategy are presented in this paper. Based on these strategies, an improved PSO variant (LSERPSO) is developed through a local search and ring topology strategy. The new scaling mutation strategy involved an exploration and exploitation balance focusing on mutation operation. A collection of elite individuals is maintained such that an array of current particles can learn from them. A ring topology-based neighborhood structure is adopted to maintain the population diversity and to reduce the possibility of particles being trapped in local optima. Finally, a quasi-Newton-based local search is incorporated to enhance the fine-grained capability. The effects of these proposed strategies and their cooperation are verified step by step. The performance of LSERPSO is comprehensively studied using IEEE CEC2015 benchmark functions.

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem (DHPFSP) with minimizing makespan and total energy consumption (TEC). To solve this NP-hard problem, this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm (CCSPEA) which contains the following features: 1) An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence. 2) A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution. 3) A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric. 4) A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search. 5) Five local search strategies based on problem knowledge are designed to improve convergence. 6) A problem-based energy-saving strategy is presented to reduce TEC. Finally, to evaluate the performance of CCSPEA, it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark. The numerical experiment results demonstrate that 1) the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population. 2) The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration. 3) The problem-based initialization, local search, and energy-saving strategies can efficiently reduce the makespan and TEC. 4) The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

Solving or approximating the linear sum assignment problem (LSAP) is an important step of several constructive and local search strategies developed to approximate the graph edit distance (GED) of two attributed graphs, or more generally the solution to quadratic assignment problems. Constructive strategies find a first estimation of the GED by solving an LSAP. This estimation is then refined by a local search strategy. While these search strategies depend strongly on the initial assignment, several solutions to the linear problem usually exist. They are not taken into account to get better estimations. All the estimations of the GED based on an LSAP select randomly one solution. This paper explores the insights provided by the use of several solutions to an LSAP, refined in parallel by a local search strategy based on the relaxation of the search space, and conditional gradient descent. Other generators of initial assignments are also considered, approximate solutions to an LSAP and random assignments. Experimental evaluations on several datasets show that the proposed estimation is comparable to more global search strategies in a reduced computational time.

A single-machine scheduling problem that minimizes the total weighted tardiness with energy consumption constraints in the actual production environment is studied in this paper. Based on the properties of the problem, an improved particle swarm optimization (PSO) algorithm embedded with a local search strategy (PSO-LS) is designed to solve this problem. To evaluate the algorithm, some computational experiments are carried out using PSO-LS, basic PSO, and a genetic algorithm (GA). Before the comparison experiment, the Taguchi method is used to select appropriate parameter values for these three algorithms since heuristic algorithms rely heavily on their parameters. The experimental results show that the improved PSO-LS algorithm has considerable advantages over the basic PSO and GA, especially for large-scale problems.

Feature selection is an important part of data mining, image recognition and other fields. The efficiency and accuracy of classification algorithm can be improved by selecting the best feature subset. The classical feature selection technology has some limitations, and heuristic optimization algorithm for feature selection is an alternative method to solve these limitations and find the optimal solution. In this paper, we proposed a Multi Strategy Grey Wolf Optimizer algorithm (MSGWO) based on random guidance, local search and subgroup cooperation strategies for feature selection, which solves the problem that the traditional grey wolf optimizer algorithm (GWO) is easy to fall into local optimization with a single search strategy. Among them, the random guidance strategy can make full use of the random characteristics to enhance the global search ability of the population, and the local search strategy makes grey wolf individuals make full use of the search space around the current best solution, and the subgroup cooperation strategy is very important to balance the global search and local search of the algorithm in the iterative process. MSGWO algorithm cooperates with each other in three strategies to update the location of grey wolf individuals, and enhances the global and local search ability of grey wolf individuals. Experimental results show that MSGWO can quickly find the optimal feature combination and effectively improve the performance of the classification model.