CURRENT STATE OF BIO HEURISTICS: CLASSIFICATION, BENCHMARKING, APPLICATION AREAS
Abstract
The purpose of this article is to analyze the current state of research in the field of development
of algorithms inspired by nature, including categorization, classification, testing, citation,
and application areas. A new multi-level classification system based on the following features is
presented: the criterion of conformity to a natural metaphor, structural, behavioral, search, component,
and evaluation criteria. The classification of bio heuristics involves the systematic assignment
of each bio heuristics to one and only one class within a system of mutually exclusive and
non-overlapping classes. Categorization allows an objective approach to the choice of bio heuristics.
For each bio heuristics there are specific tasks with which it copes well. Knowing these relationships
is important for the purposeful application of bio heuristics. An example of classification
is considered. It is noted that the most informative classification criterion is the behavioral criterion,
the most cited class of bio heuristics are swarm intelligence algorithms, and the most cited bio
heuristics is the PSO particle swarm algorithm. Modern approaches to benchmarking of bio heuristics
are presented: discrete and continuous optimization problems, as well as optimization engineering
problems. There is a tendency to compare the performance of bio heuristics using statistical
hypothesis testing on benchmarks. The tasks successfully solved by bio heuristics in such areas
as engineering design, image processing and computer vision, computer networks and communications,
energy and energy management, data analysis and machine learning, robotics, medical
diagnostics are systematized. There is a tendency to hybridize bio heuristics in one optimizer.
However, convincing evidence is required that the results compensate for the increase in complexity
compared to individual algorithms. Optimization problems requiring further research are noted:
dynamic and stochastic optimization problems; multicriteria optimization problems; multimodal
optimization problems; multidimensional optimization problems; memetic optimization
problems in which a variety of search algorithms are combined; optimization problems and adaptation
of bio heuristics parameter settings to achieve a balance between the convergence rate and
the diversification of the solution search space.
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