Systematic review and cutting-edge applications of prominent heuristic optimizers in sustainable energies.

In recent decades, significant research has been devoted to optimizing sustainable energies to improve their technical performance, economic profitability, and social acceptance. Despite the development and application of numerous heuristic algorithms in the field of sustainable energy, a systematic review of the most popular solvers remains scarce. Therefore, this study developed a Boolean logic-based program in three keyword blocks to conduct a literature search for the applications of heuristic solvers in sustainable energy, investigating their geographic- and topic-based popularity and exploring the factors contributing to their success. After describing the basic concepts and terminology of optimization, the generic applications of 28 standard heuristic algorithms in the current literature on renewable energy were assessed, from which particle swarm optimization and genetic algorithms exhibited superior performance. Therefore, the history, structure, and variants of these two algorithms were extensively analyzed to understand the reasons behind their frequent applications in three major sustainable engineering fields: energy integration, buildings, and transportation. The search results revealed that the distinguished articles typically employed the multiobjective and hybrid variants of the two algorithms. The primary impetus for their development was the familiarity of the employing communities rather than the type of optimization problems or search spaces. The first authors were mainly affiliated with China, India, Iran, and the United States, whereas the active higher education institutes were distributed in several regions without a regular pattern. Considering their productive role in publishing advanced research, open-source journals are expected to spearhead future research in this field. [ABSTRACT FROM AUTHOR]