Cultural Weight-Based Fish School Search: A Flexible Optimization Algorithm For Engineering
João Luiz Vilar Dias, M. A. S. Galindo, Fernando B. Lima-Neto · 2021
Many real-life engineering applications are optimization problems. To find the best configuration of variables to minimize costs and maximize efficiency are typically used engineering software as CAD, CAE and CAM. In this context, Machine Learning can be used to automate and improve this type of application. This despite, those searches not seldomly evoke unreliable areas and suggest risky solutions. Because of inaccuracies, volatility, unfeasibility, and specificities of real environments, the easy incorporation of cultural practices (i.e. normative, situational, domain and historic knowledge) and as well as the production of multiple acceptable solutions for a problem is always welcome, especially in Engineering. The present article put forward a hybridization of a multi-modal algorithm (Weight-Based Fish School Search - wFSS) with Cultural Algorithms' belief space. New cwFSS is able to guide the optimization also considering normative knowledge from experts, technical literature and problem domain readily available knowledge to prevent the incorporation of constraints into the fitness function. We also evaluated the use of temporal knowledge to guide the simulation. The proposed method was tested in a thermal power plant efficiency optimization and compared with standard wFSS and the Niching Migratory Multi-Swarm Optimizer (NMMSO), winner of CEC'2015 niching competition. As results, cwFSS has outperformed at times NMMSO about time, fitness and variability, as well as traditional wFSS about time, stability, safeness and variability of the multimodal solutions. By avoiding penalties, the appropriation of a priori search directly into the search can effectively and elegantly help better support for engineering decisions.