Meta-heuristic algorithm for binary dynamic optimisation problems and its relevancy to timetabling
نویسندگان
چکیده
1 Data Mining and Optimisation Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia 2 Faculty of Information & Communication Technologies, Swinburne University of Technology, Victoria 3122, Australia. 3 The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih Selangor, Malaysia. [email protected], [email protected], [email protected]
منابع مشابه
A New Solution for the Cyclic Multiple-Part Type Three-Machine Robotic Cell Problem based on the Particle Swarm Meta-heuristic
In this paper, we develop a new mathematical model for a cyclic multiple-part type threemachine robotic cell problem. In this robotic cell a robot is used for material handling. The objective is finding a part sequence to minimize the cycle time (i.e.; maximize the throughput) with assumption of known robot movement. The developed model is based on Petri nets and provides a new method to calcul...
متن کاملA discrete-event optimization framework for mixed-speed train timetabling problem
Railway scheduling is a complex task of rail operators that involves the generation of a conflict-free train timetable. This paper presents a discrete-event simulation-based optimization approach for solving the train timetabling problem to minimize total weighted unplanned stop time in a hybrid single and double track railway networks. The designed simulation model is used as a platform for ge...
متن کاملIntroducing a new meta-heuristic algorithm based on See-See Partridge Chicks Optimization to solve dynamic optimization problems
The SSPCO (See-See Particle Chicks Optimization) is a type of swarm intelligence algorithm derived from the behavior of See-See Partridge. Although efficiency of this algorithm has been proven for solving static optimization problems, it has not yet been tested to solve dynamic optimization problems. Due to the nature of NP-Hard dynamic problems, this algorithm alone is not able to solve such o...
متن کاملClustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization
So far, various optimization methods have been proposed, and swarm intelligence algorithms have gathered a lot of attention by academia. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clus...
متن کاملCo-evolving add and delete heuristics
Hyper-heuristics are (meta-)heuristics that operate at a high level to choose or generate a set of low-level (meta-)heuristics to solve difficult search and optimisation problems. Evolutionary algorithms are well-known natureinspired meta-heuristics that simulate Darwinian evolution. In this article, we introduce an evolutionary-based hyper-heuristic in which a set of low-level heuristics compe...
متن کامل