Optimisation problems can be found in every area of science and technology. Real-world optimisation problems are typically constrained, multi-modal, multi-objective and have mixed discrete-continuous decision variables.
From an optimisation perspective, solving such complex optimisation problems is very formidable and demanding. Among different strategies, metaheuristics are the most commonly used techniques for solving real-world optimisation problems, and they generally lead to satisfactory outcomes. Most metaheuristic optimisation algorithms take inspiration from natural phenomena.
A large amount of research effort is being put into developing advanced efficient metaheuristic optimisation algorithms for solving optimisation problems. This special issue mainly aims to cover novel metaheuristic optimisation algorithms and their applications to real-world problems.
Suitable topics include, but are not limited to, the following:
- Novel bio-inspired metaheuristic optimisation algorithms
- Engineering applications of bio-inspired metaheuristic optimisation algorithms
- Constraint handling strategies in bio-inspired metaheuristic optimisation algorithms
- Handling multimodal problems with metaheuristic optimisation algorithms
- Parameter selection in bio-inspired metaheuristic optimisation algorithms
- Hybrids of metaheuristic optimisation algorithms
- Performance of metaheuristic optimisation algorithms in uncertain environments
- Comparison of the performance of metaheuristics in engineering optimisation problems
- Reviews on metaheuristic optimisation algorithms
- Swarm intelligence-based optimisation versus evolutionary-based optimisation
Important Dates
Full paper submission due: 7 April, 2016
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