Recently, meta-heuristic optimization algorithms have enhanced resource efficiency, facilitated
informed decision-making, and addressed complex problems involving multiple variables and
constraints in engineering and science fields. However, numerous handicaps are reported on the
performance of a quite number of these optimizers, such as local solution trapping, slow convergence
and the requirements for elevated storage and computation capability. This article proposes a
novel, simple, and elaborate remedy for the reported deficiencies of meta-heuristic optimizers. This
deficiency is accomplished by proposing a hybrid optimizer composed of an ambiguous optimizer
and Artificial Intelligence (AI). The performance of the proposed technique is evaluated using four
different meta-heuristic optimizers: Genetic Algorithm (GA), Particle Swarm Optimization (PSO),
Teaching–Learning-Based Optimization (TLBO), and Artificial Gorilla Troops Optimization (AGTO).
These optimizers range from the mature to the recently evolved. These meta-heuristic optimizers
validate the proposed solver and confirm its applicability to any meta-heuristic optimization algorithm.
Economic Dispatch (ED) of the IEEE 30-bus system is utilized to evaluate the performance of the
proposed solver. The comprehensive results demonstrate the superiority, reliability, and adequacy
of the proposed technique. It consistently converges to the global optimum solution, achieving the
minimum energy cost of the system under concern while requiring the fewest iterations and minimal
computational requirements.