Metaheuristics Algorithms and Their Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".
Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 5686
Special Issue Editor
Special Issue Information
Dear Colleagues,
Metaheuristic optimization algorithms, in which heuristics having proven their ability in solving various optimization problems are coordinated by a higher-level algorithm, are widely used in solving engineering, financial, and optimal control problems, as well as those of clustering, classification, and deep machine learning. They are specifically designed to search, generate, or select a heuristic result that can provide a good enough solution to an optimization problem, especially when the information is incomplete or the computing power is limited. Among metaheuristic optimization algorithms, various groups are conventionally distinguished, e.g., evolutionary methods, swarm intelligence methods, algorithms generated by the laws of biology and physics, multistart, multiagent, memetic, human-based, etc.. The classification is conditional, since the same algorithm can belong to several groups at once. Evolutionary methods, in which the search process is associated with the evolution of a solutions set, namely, populations, are widely used. Swarm intelligence algorithms have gained great popularity, in which swarm members (solutions) exchange information during the search process, use information about the absolute leaders and local leaders among neighbors of each solution, and their own best positions. A significant number of studies are related to nature-inspired and bioinspired methods that imitate the characteristic features of the behavior of flocks of various birds, fish, and animals, and groups of insects during foraging, migration, and hunting. A special place is occupied by metaheuristic algorithms based on the laws of physics and biology, as well as taking into account the specifics of human interaction in society.
Metaheuristics algorithms are used for combinatorial optimization, in which an optimal solution is sought over a discrete search space. An example problem is that of the travelling salesman, where the search space of candidate solutions grows faster than exponentially possible as the size of the problem increases, which makes an exhaustive search for the optimal solution infeasible. Metaheuristics are also widely used for job-shop scheduling and job selection problems.
We invite you to submit high-quality papers to this Special Issue on “Metaheuristics Algorithms and Their Applications”, with subjects covering the whole range from theory to applications.
Prof. Dr. Andrei Panteleev
Guest Editor
Manuscript Submission Information
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Keywords
- metaheuristic algorithms
- evolutionary algorithms
- swarm intelligence algorithms
- nature-inspired optimization
- bioinspired algorithms
- physics-based algorithms
- human-based algorithms
- memetic algorithms
- multistart algorithms
- stochastic search
- hyperheuristics
