Bio-inspired Optimization: metaheuristic algorithms for optimization
- URL: http://arxiv.org/abs/2003.11637v1
- Date: Mon, 24 Feb 2020 13:26:34 GMT
- Title: Bio-inspired Optimization: metaheuristic algorithms for optimization
- Authors: Pravin S Game, Dr. Vinod Vaze, Dr. Emmanuel M
- Abstract summary: In today's day and time solving real-world complex problems has become fundamentally vital and critical task.
Traditional optimization methods are found to be effective for small scale problems.
For real-world large scale problems, traditional methods either do not scale up or fail to obtain optimal solutions or they end-up giving solutions after a long running time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In today's day and time solving real-world complex problems has become
fundamentally vital and critical task. Many of these are combinatorial
problems, where optimal solutions are sought rather than exact solutions.
Traditional optimization methods are found to be effective for small scale
problems. However, for real-world large scale problems, traditional methods
either do not scale up or fail to obtain optimal solutions or they end-up
giving solutions after a long running time. Even earlier artificial
intelligence based techniques used to solve these problems could not give
acceptable results. However, last two decades have seen many new methods in AI
based on the characteristics and behaviors of the living organisms in the
nature which are categorized as bio-inspired or nature inspired optimization
algorithms. These methods, are also termed meta-heuristic optimization methods,
have been proved theoretically and implemented using simulation as well used to
create many useful applications. They have been used extensively to solve many
industrial and engineering complex problems due to being easy to understand,
flexible, simple to adapt to the problem at hand and most importantly their
ability to come out of local optima traps. This local optima avoidance property
helps in finding global optimal solutions. This paper is aimed at understanding
how nature has inspired many optimization algorithms, basic categorization of
them, major bio-inspired optimization algorithms invented in recent time with
their applications.
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