A hyper-heuristic algorithm is a general solution framework that adaptively selects the optimizer to address complex problems. A classical hyper-heuristic framework consists of two levels, including the high-level heuristic and a set of low-level heuristics. The low-level heuristics to be used in the optimization process are chosen by the high-level tactics in the hyper-heuristic. In this study, a Cooperative Multi-Stage Hyper-Heuristic (CMS-HH) algorithm is proposed to address certain combinatorial optimization problems. In the CMS-HH, a genetic algorithm is introduced to perturb the initial solution to increase the diversity of the solution. In the search phase, an online learning mechanism based on the multi-armed bandits and relay hybridization technology are proposed to improve the quality of the solution. In addition, a multi-point search is introduced to cooperatively search with a single-point search when the state of the solution does not change in continuous time. The performance of the CMS-HH algorithm is assessed in six specific combinatorial optimization problems, including Boolean satisfiability problems, one-dimensional packing problems, permutation flow-shop scheduling problems, personnel scheduling problems, traveling salesman problems, and vehicle routing problems. The experimental results demonstrate the efficiency and significance of the proposed CMS-HH algorithm.

/pdf/a-novel-cooperative-multi-stage-hyper-heuristic-for-1g3ip1soh0.pdf

A Novel Cooperative Multi-Stage Hyper-Heuristic for Combination Optimization Problems

Network‐based robust event‐triggered control for continuous‐time uncertain semi‐Markov jump systems

A collaborative scheduling method for cloud computing heterogeneous workflows based on deep reinforcement learning

Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories

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Permutation flow shop scheduling problems (PFSPs) with many objectives are highly important scheduling problems in operations research. They have attracted substantial attention from enterprise operators and researchers in recent years. In this article, a membership function is used to connect the function values of many-objective optimization problems with a fuzzy membership degree. An approach for mapping function values to fuzzy sets is presented. Thus, the fuzzy relative entropy (FRE) can be employed in many-objective PFSPs. A many-objective PFSP model with four objectives, namely, the makespan, total tardiness, inventory holding cost, and energy consumption cost, is established. The optimal foraging algorithm (OFA) with FRE (OFA/FRE) is proposed for solving many-objective PFSPs. FRE is used as the fitness strategy of OFA. OFA/FRE is evaluated in three classification experiments and compared with five algorithms on the Walking-Fish-Group test suite, PFSP benchmark instances, and a practical PFSP for machine tool components. The results demonstrate that OFA/FRE is a promising tool for many-objective PFSP. The FRE can be used in scheduling problems and in many-objective optimization problems.

Optimal Foraging Algorithm That Incorporates Fuzzy Relative Entropy for Solving Many-Objective Permutation Flow Shop Scheduling Problems

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Optimal Foraging Algorithm That Incorporates Fuzzy Relative Entropy for Solving Many-Objective Permutation Flow Shop Scheduling Problems