**1. Introduction**

Heat exchangers are systems used to transfer heat between fluids with different temperatures. These devices have vast applications in many areas, such as refrigeration, heating, and air conditioning systems, power plants, chemical processes, food industry, automobile radiators, and waste heat recovery units. Heat exchangers can be classified according to different criteria such as construction, flow arrangement, heat transfer mechanism, etc [1]. The heat exchanger design can be divided into two main categories, thermal and hydraulic design and mechanical design. In thermal and hydraulic design, the focus is on calculating an adequate surface area transfer a certain amount of heat, pressure dope, pumping power work, etc. The goal of the mechanical design is to design the mechanical integrity of the exchanger, as well as designing various pressure and non-pressure components. In this chapter, the thermal and hydraulic design of heat exchangers is presented. To achieve better performance of heat exchangers, they optimize based on their application. Heat exchanger optimization can be performed using different optimization algorithms. Since most heat exchanger optimization problems are nonlinear, using traditional methods such as linear and dynamic programming and steepest descent may not lead to the desired solution and may even fail. Also, most traditional methods need gradient information to solve an optimization problem. On the other hand, advanced optimization algorithms are developed, which are gradient-free. Several

advanced optimization methods, such as genetic algorithm, non-nominated sorting genetic algorithm, bio-geography-based optimization, particle swarm optimization, Jaya algorithm, and teaching-learning-based optimization, can be more efficient in solving an optimization problem. However, each of these methods has its advantages and disadvantages, which are discussed in the optimization section. In this chapter, genetic algorithm and particle swarm optimization are discussed in detail due to the vast applications that arise from their acceptable accuracy, as well as short computational time [2].
