Abstract
This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems.
Highlights
Inspired by the collective behavior in the natural and biological phenomenon in organisms, researchers have developed a plethora of nature-inspired and bio-inspired meta-heuristic algorithms
This paper presented a novel novel bio-inspired bio-inspired population-based population-based optimization optimization algorithm algorithm called Blood Coagulation Algorithm (BCA)
The proposed BCA mimics the process of blood called Blood Coagulation Algorithm (BCA)
Summary
Inspired by the collective behavior in the natural and biological phenomenon in organisms, researchers have developed a plethora of nature-inspired and bio-inspired meta-heuristic algorithms. Nature-inspired meta-heuristic algorithms are designed to tackle optimization problems by simulating some natural phenomena In the literature, these algorithms have been organized into four groups: Evolution-based, swarm-based, physics-based, and human behavior-based algorithms [1]. There is still a room to develop new and powerful nature- or bio-inspired meta-heuristic algorithms besides improving the existing ones, either to tackle the existing complex optimization problems more efficiently or to solve new problems This motivates our attempt to propose a novel optimizer to compete with the existing algorithms. The optimization results reveal that BCA is very competitive compared to the state-of-the-art algorithms This population-based stochastic meta-heuristic exhibits outstanding performance and optimization capability for all the benchmark test functions and real-world engineering problems considered in the present study.
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