A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

Abstract

The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE) algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS), the satisfactory solution is obtained by using a decision-making method for multiple attributes.

A neighborhood‐adaptive state transition algorithm for operational optimization of residue hydrogenation fractionation process

“…Heidari et al 36 proposed Harris Hawks Optimization (HHO) and achieved good results on the Rolling element bearing design problem. In addition, the niched Pareto genetic algorithm (NPGA), 37 the Pareto archive evolution strategy (PAES), 38 the artificial bee colony algorithm with memory algorithm (ABCM), 39 the enhanced elephant herding optimization 40 algorithm, a hybrid DE algorithm using the simplex method (SM-DEMO), 41 a chaotic Krill Herd (CKH) method 42 and many other intelligent optimization algorithms are subsequently used in complex industrial processes. However, the basic ideas of these optimization methods are inspired by the social behavior of animal groups, so their optimization has the characteristics of group evolution.…”

Optimization of both operating costs and energy efficiency in the alumina evaporation process by a multi‐objective state transition algorithm

“…The representative methods of this approach have the rank-based fitness assignment method of genetic algorithms, [15] the niched Pareto genetic algorithm (NPGA), [16] the non-dominated ranking genetic algorithm (NSGA) [17] and its classical improved version NSGA-II, [18] the micro-genetic algorithm, [19] the Pareto archive evolution strategy (PAES), [20] the strength Pareto evolutionary algorithm (SPEA) [21] and its improved version SPEA2, [22] the incremental multi-objective evolutionary algorithm (MOEA), [23] and the MOEA based on decomposition techniques. [24][25] In addition to the traditional EAs, other evolutionary metaheuristics have also been proposed and used to successfully solve the MOPs, such as Scatter Search (SS), [26][27] Particle Swarm Optimization (PSO), [28][29][30] Differential Evolution (DE), [31][32][33] and others. By combining different ideas or meta-heuristics, the proposed algorithm may further improve the effectiveness of methods in order to overcome the inherent limitations of a single evolutionary algorithm or meta-heuristic.…”

“…Although the MOPs and constraint handling techniques have received much attention, the CMOPs are still challenging in practice when considering the constraints of the technological process. Wang [32] designed a hybrid DE algorithm using the simplex method (SM-DEMO) for bauxite grinding-classification operation. The proposed algorithm is formed by combining the simplex method and an elite population mechanism to ensure that some infeasible solutions with better performances are allowed to take part in the evolutionary process.…”