## 3 Artificial Intelligence (AI) Techniques in Power Systems
### Evolutionary Methods
#### 3.4.2 Metaheuristic Optimization Technique  
Particle swarm optimization (PSO), a population-based evolutionary technique, offers several notable advantages over alternative optimization methods:  
* PSO can handle objective functions that may not be continuous, convex, or differentiable, as it operates as a non-gradient, derivative-free approach, unlike deterministic methods [53]. * PSO utilizes the fitness function value to guide the search for optimality in the problem space rather than relying on derivative information (first and second order) to locate an optimal solution [54]. * By utilizing the fitness function value, PSO circumvents the need for approximations and assumptions often employed by conventional optimization techniques on problem objectives and constraint functions. * Due to its stochastic nature, PSO can effectively address specific optimization problems characterized by objective functions with stochastic and noisy attributes. * In contrast to deterministic approaches, the quality of a solution produced by PSO is not dependent on the initial solution. * Being a population-based search technique, PSO enables the algorithm to evaluate multiple solutions in a single iteration, thereby reducing the risk of becoming trapped in local minima [55]. * The adaptability of the PSO algorithm allows for integration and hybridization with other approaches, whether deterministic or heuristic, when necessary. * PSO requires fewer parameters to calibrate and adjust than many other metaheuristic approaches. * Due to its utilization of straightforward mathematics and Boolean logic operations, the PSO method is generally easy to understand, implement, and program. Various heuristic methods, such as genetic algorithms, simulated annealing, evolutionary programming, and ant colony optimization, compete with PSO alongside conventional gradient-based optimization algorithms. While these methods may handle various optimization challenges similar to PSO, they often have significant drawbacks:  
* Additional parameter adjustment is necessary. * They frequently demand longer computation times. * Highly complex programming abilities are needed to create and adapt competing algorithms to fit various categories of optimization problems. Most demand a sizeable population of members, resulting in more fitness evaluations. Some strategies involve binary conversion instead of working with actual valued variables directly. The following algorithms have advantages over PSO: MATLAB's genetic algorithm, simulated annealing, and the commercial versions of Excel premium solver (evolutionary programming). However, it is worth noting that some of these methods are only available in commercial versions. An extensive collection of books and academic articles provides in-depth coverage of various competing approaches, particularly in the case of genetic algorithms and evolutionary programming [56]. The same category's additional heuristic methods are enumerated in [57]. These methods are becoming more widely used primarily because of their dependability, simplicity, and capacity to handle increasingly precise models without unpleasant approximations. Like other metaheuristic optimizers, PSO's main flaws are its lack of a solid mathematical foundation and inability to guarantee theoretically global optimum solutions. Many common benchmark optimization problems that academics use to test new global optimization strategies have shown that PSO performs well. Reference [58] is a valuable resource that has examined and investigated the promising convergence properties of the PSO algorithm, where [PERSON] and [PERSON] were successful in laying the mathematical groundwork to describe how a simplified PSO model behaves while looking for the best answer. However, there is a need for further research to address the remaining challenges in the use of the particle swarm optimization (PSO) algorithm, such as the social influence component and the development of general guidelines for adjusting its parameters to suit various optimization problems.