Abstract
Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method based on entropy and security market line, fully considering the characteristics scheduling problems. It consists of two key parts: locus-entropy strategy and parameter-control strategy. Firstly, the entropy on each genetic locus is calculated to accurately evaluate the population status of scheduling algorithms. Then, a parameter-control strategy based on the conception of security market line is proposed to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions. The strategy maintains the solutions of good quality and eliminate the solutions of poor quality by using locus-entropy as feedback. Through our method, the balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and characteristics. These strategies are tightly linked to adjust parameters adaptively without introducing new parameters, so that meta-heuristic algorithms equipped with the proposed approach are able to find a better solution. Moreover, our parameter control method is universal for meta-heuristic algorithms. The proposed approach is hybrid with genetic algorithm and particle swarm optimization. The hybrid algorithms are first compared with the standard algorithms in multipeak benchmark functions, then with other variants of the standard algorithms in real-world single and multi-objective scheduling problems. The results demonstrate that the proposed approach is valid for different kinds of algorithms to enhance the performance of solving a variety of scheduling problems.
Highlights
Scheduling problems are a kind of combinatorial optimization problems encountered in various fields, whose solutions are widely applied to help decision-makers gain huge benefits at lower costs under various constraint conditions [1]–[3]
In order to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions, a parameter-control strategy based on security market line (SML) is proposed to adjust all parameters adaptively
The results indicate that the approach helps meta-heuristic algorithms find better solutions in different types of scheduling problem regardless of the termination conditions
Summary
Scheduling problems are a kind of combinatorial optimization problems encountered in various fields, whose solutions are widely applied to help decision-makers gain huge benefits at lower costs under various constraint conditions [1]–[3]. We investigate a self-adaptive parameter control method for scheduling problems. It consists of two key parts, including a locus-entropy strategy and a parametercontrol strategy. The parameters are adjusted adaptively based on the entropy of each genetic locus and the conception of security market line (SML) It is a universal method, which can be hybrid with meta-heuristic algorithms. The balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and different characteristics These strategies are tightly linked in adjusting parameters adaptively without introducing new parameters. In order to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions, a parameter-control strategy based on SML is proposed to adjust all parameters adaptively.
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