Cellular teaching-learning-based optimization approach for dynamic multi-objective problems

Many real-world optimization problems involve several conflicting objectives that must be optimized simultaneously. Furthermore, most optimization problems have a dynamic structure and change over time. In addition to trying to establish trade-offs among conflicting objectives and explore a diverse set of solutions on a Pareto-optimal front, a dynamic multi-objective optimization (DMOO) algorithm tries to detect changes and track them, using the knowledge of prior environments to converge to the new Pareto-optimal front more quickly. In this paper, a cellular automata-based approach is first proposed for managing and evaluating solutions during the optimization process. Then, using the above approach and the teaching-learning-based optimization algorithm, two new algorithms are introduced for DMOO problems. The first algorithm works to optimize the objectives all at once in a multi-objective manner, while the second algorithm uses the vector evaluated technique to evolve solutions in collaborative single-objective optimization units, and then analyzes them from a multi-objective perspective. These algorithms have been evaluated and compared with some other DMOO algorithms for some standard benchmark problems. The results indicate their superiority in many of the experiments.