# Simulated Annealing with Exploratory Sensing for Global Optimization
## 1 Introduction
Moreover, MSA uses a real-time monitoring strategy to check whether a solution has been trapped in a local optimum. Moreover, as a solution for the slow convergence problem, an Adaptive simulated annealing genetic algorithm (ASAGA) based on a mutative scale chaos optimization strategy is proposed in [8]. The algorithm can benefit from the parallel searching structure of the GA and the probabilistic jumping property of the SA, besides implementing an adaptive crossover and mutation operators. The results proved finding the global one more quickly. In [9], an enhanced simulated annealing algorithm was developed by integrating \"directional search\" and \"memory\" capabilities. The algorithm performance is improved by directing the search based on a better understanding of the configuration space's current neighborhood. A Tabu Search with memory concept and an enhanced annealing method is tested and improved the convergence rate and quality. A multi-objective simulated annealing (MOSA) and an adaptive memory procedure (MOAMP) is proposed in [10]. These algorithms are tested by finding a set of non-dominated solutions of hybrid flow shop scheduling problems concerning both objectives of total setup time and cost. Without memory, the simulated annealing is time-consuming and has difficulty controlling the temperature and transition number. In [11], an annealing framework with a learning memory is implemented. That proposed framework showed reasonable confidence in the solution quality. Moreover, there are several attempts to improve the SA performance through hybridization with different metaheuristics [12; 13; 14], or thought invoking restart strategies [15; 16; 17]. On the other hand, several alternative nature-inspired optimization algorithms have been proposed to deal with general global optimization problems, such as genetic algorithms [18; 19; 20], evolution strategies [21; 22], evolutionary programming [23; 24], tabu search [25], memetic algorithms [26; 27; 28], differential evolution [29; 30; 31; 32], particle swarm optimization [33; 34; 35; 36; 37], scatter search [38; 39], ant colony optimization [40; 41; 42], artificial bee colony [43; 44], variable neighborhood search [45; 46], and hybrid approaches [47; 48; 49; 50; 51; 52]. Besides these well-known techniques, other new methods which recently proposed including multi-verse optimizer [53], fuzzy adaptive teaching [54], whale optimization algorithm [55], wolf preying behavior [56], and drone squadron optimization [57]. In this paper, we propose a two-phase methodology using automatic diversification and intensification based on memory and sensing tools in this work. Actually, one of the challenging issues in two phases hybrid algorithms is the timing of the switch between the diversification-oriented algorithm and the local-oriented search algorithm leading to the need for incorporating diversity measures [51]. Therefore, an efficient metaheuristic approach for finding a global optimum of optimization problems is presented to achieve a wide and deep search. The proposed method is called simulated annealing with exploratory sensing (SAES) that integrates different features of several well-known heuristics. The proposed algorithm's core is a simulated annealing module that is integrated with exploration and diversification schemes to enhance the search process using adaptive search memories. In particular, a Gene Matrix (GM) concept [58; 59] is constructed to sample the search space, guide the search process, and to accelerate the method termination. The GM is used as a diversification tool to record a history of space's visited partitions. New solutions will be created in the non-visited partitions; therefore, the search process is directed to visit individuals in these partitions during the diversification process. After having the GM matrix filled with ones giving an indicator of visiting most partitions, an intensification process starts. More intensification as a local search in its region will be carried out from the best-found solution.