In this study, a hybrid strategy of multiobjective differential evolution (hybrid-MODE) algorithm is proposed that consists of an evolutionary algorithm for global search and a deterministic algorithm for local search. To begin, the proposed algorithm is tested for its performance using a benchmark test function (KUR) as case study 1 with the nondominated sorting genetic algorithm-II (NSGA-II) (both binary- and real-coded versions), strength Pareto evolutionary algorithm (SPEA), Pareto archived evolutionary strategy (PAES), multiobjective differential evolution (MODE), and an improved strategy of MODE algorithms. Subsequently, the multiobjective optimization of an industrial adiabatic styrene reactor is carried out as case study 2, employing a prevalidated model using the hybrid-MODE algorithm and an improved strategy of MODE. Four cases (three sets of two-objective optimization, cases 1-3, and one set of three-objective optimization, case 4) are considered consisting of simultaneous maximization of styrene productivity, selectivity, and yield with four decision variables and two constraints. The proposed algorithm converges to a better set of nondominated solutions (possibly a Pareto front) as compared to the nondominated solutions obtained using NSGA and an improved strategy of MODE algorithms. The limitations of previous studies are reported in terms of key decision variables. The hybrid strategy of MODE is found to converge to the true Pareto front more rapidly (in fewer function evaluations), resulting in a well-diversified Pareto front as compared to the stand-alone evolutionary approach.
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