## 2 Related Work
### Optimization Algorithms for Multi-Modal Function  
Population or single solution search-based optimization algorithms (viz., meta, hyper-heuristic) have solid global search ability. Typical examples include inter alia evolutionary and swarm intelligence algorithms, such as the genetic algorithm (GA) [33], differential evolution (DE) algorithm [34], particle swarm optimization (PSO) algorithm [35], comprehensive learning particle swarm optimization (CLPSO) [36] and artificial bee colony (ABC) algorithm [37]. These algorithms are essentially methods by trial and error. It can take \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strategies** & \multicolumn{5}{c}{**Objectives**} \\  & **Sensing Coverage** & **Network Connectivity** & **Simplicity** & **Robustness** & **Energy Balance** \\ \hline RIS [18] & \(k\)-coverage,asymptotic & \(\times\) & \(\times\) & Y & Y \\ BS [20] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Y \\ LDAS [15] & \(1\)-coverage & \(\times\) & \(\times\) & \(\times\) & Y \\ PEAS [16] & \(\times\) & \(1\) & \(\times\) & \(\times\) & \(\times\) \\ PECAS [17] & \(\times\) & \(1\) & \(\times\) & \(\times\) & Y \\ CCP [21] & \(k\)-coverage & \(k\) & \(\times\) & \(\times\) & Y \\ ASCENT [18] & \(\times\) & \(\times\) & Y & \(\times\) & \(\times\) \\ CSS [22] & \(k\)-coverage & \(k\) & \(\times\) & Y & Y \\ LEACH-GA [23] & \(\times\) & \(\times\) & \(\times\) & Y & Y \\ IBLEACH [24] & \(\times\) & \(k\) & \(\times\) & Y & Y \\ ESS [25] & \(\times\) & \(\times\) & \(\times\) & Y & Y \\ DSS [26] & \(\times\) & \(\times\) & \(\times\) & \(\times\) & Y \\ IDCA [27] & Full-coverage & \(\times\) & \(\times\) & \(\times\) & Y \\ \hline \hline \end{tabular}  
\(\times\): not mentioned.  
\end{table} 
Table 2: The studies’ classifications according to their objectives. many thousands or even millions of iterations to converge. New emerging algorithms, such as the multi-layered gravitational search algorithm (MLGSA) [38] and quantum tabu search (QTS) algorithm [39], improve classical versions by avoiding premature convergence or being trapped in local optima. When addressing practical issues, we search for the global extremum of a complicated or unknown function, but just finding one local minimum of a relatively simple but very high-dimensional function can also be a formidable challenge; for example, the Multi-modal Function Optimization (MFO) [40]. Given a multi-modal problem, the optimization task is to identify the greatest number of optimal solutions (global and local) to help the decision-maker better understand the problem at hand. It has developed many techniques to locate local optimums. These techniques are termed \"niching\" methods. A niching method can be built into a standard search-based optimization algorithm to identify several optimal or sub-optimal solutions sequentially or concurrently. Sequential approaches discover optimal solutions gradually over time, while concurrent methods encourage and maintain many stable sub-populations within a single population. Conventional niching techniques include crowding, fitness sharing, decommissioning, covariance matrix adaptation, cleaning, species conserving, and so forth. More recently, some variants of meta-heuristic algorithms, such as an ant colony system with nonlinear pheromone update (ACS-NP) [41], and comprehensive learning particle swarm optimization with local search (CLPSO-LS) [42], have incorporated niching methods. Despite niching methods' first appearance over 30 years ago (in the 1980s), niching methods (or multi-modal optimization) are being resurrected as an increasingly important research subject, attracting researchers from a broad spectrum of research areas, including Evolutionary Computation (EC) [43] and Swarm Intelligence (SI) [42]. The Fibonacci tree optimization algorithm (FTO) [44] is a sophisticated optimization algorithm. It resolves the optimal solution of the problem by alternating iterations of global scanning and local scanning. It fully utilizes computer memory to save the optimization process.