An evolutionary algorithm for multi-objective optimization of freshwater consumption in textile dyeing industry
Optimization is challenging even after numerous multi-objective evolutionary algorithms have been developed. Most of the multi-objective evolutionary algorithms failed to ﬁnd out the best solutions spread and took more ﬁtness evolution value to ﬁnd the best solution. This article proposes an extended version of a multi-objective group counseling optimizer called MOGCO-II. The proposed algorithm is compared with MOGCO, MOPSO, MOCLPSO, and NSGA-II using the well-known benchmark problem such as Zitzler Deb Thieler (ZDT) function. The experiments show that the proposed algorithm generates a better solution than the other algorithms. The proposed algorithm also takes less ﬁtness evolution value to ﬁnd the optimal Pareto front. Moreover, the textile dyeing industry needs a large amount of fresh water for the dyeing process. After the dyeing process, the textile dyeing industry discharges a massive amount of polluted water, which leads to serious environmental problems. Hence, we proposed a MOGCO-II based optimization scheduling model to reduce freshwater consumption in the textile dyeing industry. The results show that the optimization scheduling model reduces freshwater consumption in the textile dyeing industry by up to 35% compared to manual scheduling. Subjects Algorithms and Analysis of Algorithms, Computer Networks and Communications, Mobile and Ubiquitous Computing, Optimization Theory and Computation, Scientiﬁc Computing and Simulation.