An improved league championship algorithm with free search and its application on production scheduling

Abstract

An improved league championship algorithm with free search (LCAFS) is proposed to avoid the drawbacks of basic LCA, such as premature convergence, slow convergence speed. The parameters of the algorithm vary linearly along with iteration. A novel match schedule is designed to improve the competition capability for the sport teams. Furthermore, the free search operation is introduced to promote the diversity of the league. Inspired by the real league degradation, degradation mechanism is used to preserve the team elites. It is convinced by using benchmark functions that LCAFS is superior to other compared algorithms in the global searching performance and convergence speed. The proposed algorithm is finally employed as learning method of parameters in neural network to establish the shop floor production scheduling model and achieves good results.

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

“…It has been proved that manufacturing scheduling problems, essentially belonging to Job shop scheduling problems (JSP), are a class of NP-hard problems [ 59 ]. Many models and algorithms have been proposed to obtain a suboptimal solution, such as PICRO (preference-inspired chemical reaction optimization algorithm) [ 60 ], HPGA (hybrid PSO-GA algorithm) [ 61 ], PRGA-Sched (priority rule-based genetic algorithm scheduling) [ 62 ], LCAFS (league championship algorithm with free search) [ 63 ], MOGA-TIG (multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy) [ 64 ], and so on. Though an approximate optimal production plan can be effectively solved by these methods, the availability and reliability of sensor nodes is seldom followed closely.…”

An optimise ELM by league championship algorithm based on food images

“…This led us to select LCA to be used as an optimisation algorithm for the weights and biases on ELM. Numerous researchers have applied LCA in many fields [21][22][23][24].…”