Emergent nature inspired algorithms for multi-objective optimization

Brief description of the topic: Many real-world decision-making situations possess both a discrete or combinatorial structure and involve the simultaneous consideration of conflicting objectives. Problems of this kind are in general of large size and contains several objectives to be “optimized”. Although Multiple Objective Optimization is a well-established field of research, one branch, namely inspired nature metaheuristics is currently experienced a tremendous. Over the last few years, developments of new methodologies, methods, and techniques to deal with multi-objective large size problems in particular those with a combinatorial structure and the strong improvement on computing technologies (during and after the 80s) made possible to solve very hard problems with the help of inspired nature based metaheuristics. Today real-world applications of large scale multiple objective discrete models are feasible