## 3 Analysis of Literature Resources
### Metaheuristic Techniques
#### 3.3.2 Population-Based Metaheuristics  
The population-based methods are categorized into two main categories. Firstly, the EAs include Genetic Algorithms (GAs) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). GAs are optimization techniques inspired by the principles of natural selection and genetics. They operate on a population of candidate solutions and simulate biological processes such as reproduction, crossover, and mutation to iteratively evolve toward an optimal solution. In this context, each individual in the population (also called a chromosome) encodes a potential solution, typically represented through binary or real-valued gene sequences that map the specific characteristics of the problem. Its operation is based on a fitness function, which measures the quality of a solution according to defined performance metrics. The most fit individuals are selected to reproduce, combining their genetic material to form a new generation through crossover and mutation. This process continues across iterations until a stopping criterion is met, such as convergence to an acceptable solution or a fixed number of generations [69]. In job shop scheduling environments, GAs are employed to estimate energy consumption and minimize makespan by encoding and exploring the solution space efficiently [45]. To enhance performance, GAs are often combined with local search algorithms, especially in energy-efficient Job Shop Scheduling Problems (JSPs), to refine candidate solutions [67]. At each step of the process, individuals with higher fitness are more likely to be selected for the next generation. Through continuous evolution, the population gradually improves, with each successive generation achieving better performance. Eventually, the algorithm converges to a near-optimal or optimal solution, which is decoded from the best-performing individuals in the final generation [25]. Another multi-objective optimization algorithm called the NSGA-II is an EA designed to find a diverse set of solutions within complex optimization problems involving multiple conflicting objectives. It operates by simulating the process of natural selection, where the population is composed of a set of genes that represent all the operations to be scheduled and the set of candidate solutions is represented by chromosomes or individuals [70]. The solution evolves over generations to approximate the Pareto front--the set of all solutions based on non-dominance and elitism criteria [45;70]. JSPs, in particular, often involve objectives such as minimizing makespan, total tardiness, and machine changes. Due to the conflicting nature of these objectives, JSPs are commonly formulated as multi-objective optimization problems, where the aim is not to find a single optimal solution but a diverse Pareto set that balances these competing goals. Accurately approximating this Pareto front increases the complexity of designing efficient and scalable optimization techniques [79]. NSGA-II is a robust and efficient algorithm able to find a diverse set of high-quality solutions, making it a valuable tool in many practical applications. If you are working on a problem that involves multiple conflicting objectives, NSGA-II is worth considering. However, since the algorithm lacks a great local search capability, it can be inefficient, being unable to quickly solve optimization problems [67;70]. On the other hand, Swarm Intelligence (SI) refers to Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and the Fish Swarm Algorithm (FSA). Ant Colony Optimization (ACO) is a metaheuristic algorithm designed to solve discrete optimization problems [71], including multi-objective optimization tasks [63]. Originally introduced as the Ant System (AS), the ACO algorithm evolved into the more effective Ant Colony System, which improved solution feasibility by guiding the ants' path construction [38]. Inspired by the natural foraging behavior of ants, ACO uses pheromone trails and problem-specific heuristics to probabilistically guide search processes [63; 68].