Global sensing search for nonlinear global optimization

Metaheuristics are powerful and generic global search methods. Most metaheuristics methods are not fully equipped with learning processes. Therefore, most of the search history is not reused in further steps of metaheuristics. The main aim of this research is to develop a general framework for automating and enhancing the search process and procedures in metaheuristics. The proposed framework, called Global Sensing Search (GSS), utilizes search memories to equip the search with applicable sensing features and adaptive learning elements to find a better solution and explore more diverse ones. Moreover, the GSS framework applies different search conditions to check the need for using suitable intensification and/or diversification strategies and also for terminating the search. An implementation of the GSS framework is proposed to alter the structure of standard genetic algorithms (GAs). Therefore, a new GA-based method called Genetic Sensing Algorithm is presented. The computational experiments show the efficiency of the proposed methods.