Abstract
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space.
Highlights
Increasing technological development makes the accuracy becoming the most desirable element in applied modeling
Problems with pollution and numerous threats of earthquakes cause the location of homes on affected areas to be of a paramount importance. [4] presented positioning of networking systems by nature based optimization methodology. [5] presented that a model of a real-time seismic monitoring and early warning system for earthquakes can be based on devoted heuristic optimizer. [6] presented that heuristic algorithms can be applied in the design of steel structured houses that have been built in areas affected by seismic movements
The results show that proposed Polar Bear Optimization Algorithm (PBO) method along with Moth-Flame Optimization Algorithm (MFO) and Particle Swarm Optimization (PSO) are the most accurate for this problem
Summary
Increasing technological development makes the accuracy becoming the most desirable element in applied modeling. It is essential, so that the cost of the product or the amount of work will be as small as possible. The problem of accuracy comes to finding optimal solutions for given problems from engineering, architecture, medicine, etc. Through these areas, a heuristic approach to solving problems turned out to be a successful tool. A heuristic approach to solving problems turned out to be a successful tool It becomes more and more popular due to numerous features, such as speed of finding optimal solutions and low computational complexity. Problems with pollution and numerous threats of earthquakes cause the location of homes on affected areas to be of a paramount importance. [4] presented positioning of networking systems by nature based optimization methodology. [5] presented that a model of a real-time seismic monitoring and early warning system for earthquakes can be based on devoted heuristic optimizer. [6] presented that heuristic algorithms can be applied in the design of steel structured houses that have been built in areas affected by seismic movements
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