• Genetic Algorithms (GAs) and evolutionary strategies provide a cohesive optimization framework across engineering domains, including discrete structural optimization, graph matching, and design under constraints [12], [1], [8], [19], [17], [20], [9].

• Tabu Search and its extensions illustrate metaheuristic frameworks that guide local search away from local optima, enabling improved solutions for scheduling, clustering, and engineering problems [7], [2], [15].

• Simulated annealing and hybrid strategies underpin optimization of digital filters, nonlinear magnetic problems, and electromagnetic design, highlighting flexible annealing-based approaches alongside deterministic methods [5], [10], [13].

• Hybrid optimization approaches and constrained problem handling show advances in converting constrained formulations into tractable forms (augmented Lagrangian, penalty methods) to boost convergence on engineering tasks [17], [8], [10].

• Engineering optimization in power systems and electromagnetics demonstrates practical impact, addressing capacitor sizing, network reconfiguration, and EM device design with metaheuristics and optimization tools [3], [11], [4], [6].