Metaheuristic Algorithms for Combinatorial Optimization Problems
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 31 October 2024 | Viewed by 211
Special Issue Editors
Interests: operations managment; manufacturing systems; planning and control; layout; optimisation using nature inspired algorithms; supply chain management; information technology; make/engineer-to-order companies; lean manufacturing; agile manufacturing
Interests: manufacturing planning and scheduling; computer simulation; supply chain and logistics management; metaheuristics; applied statistics and operations research on industrial systems
Special Issue Information
Dear Colleagues,
Most combinatorial optimisation problems are NP-hard, which means that there are no polynomial time algorithms that can solve them in reasonable time. Large combinatorial optimisation problems may be effectively solved using metaheuristics, but it is often impossible to search the whole problem space, therefore it is not possible to guarantee an optimal solution. Metaheuristics can be classified in several ways. Those that encode problems using real variables that are used for continuous optimisation and those that use discrete variables for combinatorial optimisation. Single-point metaheuristics that use local search heuristics, e.g., Tabu Search, Simulated Annealing and Greedy Randomised Adaptive Search, whilst population-based metaheuristics, such as Genetic Algorithms, Ant Colony Optimization and Particle Swarm Optimization that produce multiple solutions that explore the search space.
This Special Issue aims to bring together state-of-the art research relating to the optimization of novel problems using established metaheuristics and clearly specified experimental designs. Research that uses real-world data or secondary data, such as benchmark datasets is welcome. Possible areas of application may include scheduling, timetabling, rostering, facilities layout design and continuous or discrete engineering optimization problems.
Prof. Christian Hicks
Dr. Pupong Pongcharoen
Guest Editors
Manuscript Submission Information
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Keywords
- metaheuristic
- optimization
- evolutionary computation
- bio-inspired computation
- scheduling
- timetabling
- facilities layout problem
- engineering optimization problems
- genetic algorithms
- particle swarm optimization
- tabu search
- simulated annealing
- ant colony optimization
