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Top1. Introduction
The study of optimization is concerned with determining the optimum solutions to specific issues. Every day, we devised a number of activities that we attempted to enhance in order to arrive at the optimum answer; for example, the route to work can be optimised based on a number of parameters such as traffic and distance. On the other hand, the design of modern cars necessitates an optimization process with a number of goals in mind, including reduced wind resistance, reduced fuel consumption, and increased motor potency (Himanshukumar, 2021; Himanshukumar & Vipul, 2022b; Himanshukumar & Vipul, 2022f). These optimal solutions are discovered by adjusting the algorithm’s parameters to give the solution a maximum or minimum value. As a result, various optimization strategies have been created in recent years with the goal of improving existing solutions (Valdez et al., 2021).
Many nature-based optimization techniques can now be discovered in the literature; it is estimated that there are over 150 various strategies, as well as modified algorithms, for finding the best outcomes on optimization problems (Valdez, 2020; Wang et al., 2021; Yan et al., 2021; Yue et al., 2021).
Evolutionary algorithms (EA), which are classified as stochastic algorithms, were created using a combination of rules and randomness to replicate a variety of natural events. The evolutionary algorithm (EA) proposed by Fogel et al. (1966), de Jong (1975), and Koza (1990), the Genetic Algorithm (GA) proposed by Holland (1975) and Goldberg (1989), the artificial immune system proposed by de Castro and von Zuben (1999), and the Differential Evolution Algorithm (DE) proposed by Storn and Price (1995) are examples of such phenomena. Simulated annealing, proposed by Kirkpatrick et al. (1983), the electromagnetism-like algorithm, pro- posed by Birbil and Fang (2003), and the gravitational search algorithm, proposed by Rashedi et al. (2011) are some other approaches based on physical processes. There are also additional methods based on animal behaviour phenomena, such as Kennedy and Eberhart’s Particle Swarm Optimization (PSO) algorithm (1995) and Dorigo et al. colony optimization (ACO) algorithm (1991).
Every EA must handle the issue of search space exploration and exploitation (Tan et al., 2009). Exploration is the act of visiting completely new sites in a search space, whereas exploitation is the process of refining those places in the vicinity of previously visited locations in order to increase the quality of their solutions. Pure exploration reduces the evolutionary process’ precision while increasing its capacity to identify new potential solutions. Pure exploitation, on the other hand, allows for the refinement of existing solutions while also driving the process to local optimal solutions.
The cuckoo search (CS) method (Yang & Deb, 2009), a revolutionary nature-inspired algo- rithm, has recently been developed for addressing complicated optimization is- sues. The CS algorithm is based on some cuckoo species’ obligate brood-parasitic behaviour. The utilisation of Le`vy flights to create new candidate solutions is one of the most powerful elements of CS. Candidate solutions are modified using this method by making a lot of tiny modifications and a few big jumps. As a result, CS can significantly improve the link between exploration and exploita- tion while simultaneously improving its search skills (Walton et al., 2013). According to recent research, CS has the potential to be significantly more efficient than PSO and GA (Yang & Deb, 2010). Mesh generation (Walton et al., 2011), embedded systems (Kumar & Chakarverty, 2011), steel frame design (Kaveh & Bakhshpoori, 2013), scheduling issues (Tein & Ramli, 2010), thermodynamics (Bhargava et al., 2013), and distribution networks (Moravej & Akhlaghi, 2013) are just a few of the engineering problems that CS has been used to tackle.