The operators of the powerful metaheuristic algorithms can efficiently explore several regions in the problem search space as well as exploit the accumulative knowledge acquired during the search process. Metaheuristic-based approaches provide a general optimization framework that can iteratively improve the current solution(s) using intelligent knowledge-acquisition operators with stochastic features controlled by tuned parameters until an optimal solution is reached. Therefore, the emergence of metaheuristic algorithms as an efficient approximation-based method acquired high attention due to its superior advantages. The ultimate objective of tackling optimization problem is not only to find any solution, but also to find a “good enough” solution. The heuristic-based approaches although they can easily find a solution for the optimization problem, and the quality of the constructed solution is not unfortunately respected. Heuristic methods are problem-specific where each optimization problem has its own heuristic methods for example, graph coloring problems use saturation algorithm heuristic methods. The traditional approximation-based methods were heuristic-based in which the optimization problem is constructively tackled element by element until a complete solution is reached. Consequently, the optimization research communities tend their attentions to utilize approximation methods for their optimization problems.Īpproximation methods have stochastic components to intelligently overcome the deterministic-based dilemmas. Thus, they are inefficient in tackling real-world problems.
Although they can find an exact solution for the optimization problem, they suffer from some dilemmas such as they cannot be used to tackle the NP-hard problems they require heavy mathematical derivation, especially for gradient-based techniques they can easily be stuck in a local optima. Traditionally, deterministic-based methods are utilized to tackle some optimization problems with small dimensions and less complexity. In order to tackle optimization problems, two types of optimization methods emerge deterministic-based and approximation-based. The optimization process can be utilized in several research domains such as health, engineering, mathematics, economics, linguistics, and science to optimize (minimize or maximize) their objective. Optimization is the process of finding the best configurations of some entities following limited resources respecting predefined constraints. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. Initially, the sensitivity of CHIO to its parameters is studied. CHIO is evaluated using 23 well-known benchmark functions. This is to determine how the newly generated solution updates its genes with social distancing strategies. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. CHIO mimics the herd immunity strategy as well as the social distancing concepts. These concepts are modeled in terms of optimization concepts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission.
In order to protect other members of society from the disease, social distancing is suggested by health experts. The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO).
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