A Novel Hybrid Algorithm for Solving Multiobjective Optimization Problems with Engineering Applications

Abstract

An effective hybrid algorithm is proposed for solving multiobjective optimization engineering problems with inequality constraints. The weighted sum technique and BFGS quasi-Newton's method are combined to determine a descent search direction for solving multiobjective optimization problems. To improve the computational efficiency and maintain rapid convergence, a cautious BFGS iterative format is utilized to approximate the Hessian matrices of the objective functions instead of evaluating them exactly. The effectiveness of the proposed algorithm is demonstrated through a comparison study, which is based on numerical examples. Meanwhile, we propose an effective multiobjective optimization strategy based on the algorithm in conjunction with the surrogate model method. This proposed strategy has been applied to the crashworthiness design of the primary energy absorption device's crash box structure and front rail under low-speed frontal collision. The optimal results demonstrate that the proposed methodology is promising in solving multiobjective optimization problems in engineering practice.

A Multi–Objective Gaining–Sharing Knowledge-Based Optimization Algorithm for Solving Engineering Problems

“…Table 7 displays the fifty different problems. RWMOP15 Spring Design [62] RWMOP16 Cantilever Beam Design [73] RWMOP17 Bulk Carrier Design [74] RWMOP18 Front Rail Design [75] RWMOP19 Multiproduct Batch Plant [76] RWMOP20 Hydrostatic Thrust Bearing Design [77] RWMOP21 Crash Energy Management for High-Speed Train Problem [78] Chemical Engineering Problems RWMOP22 Problem of Haverly's Pooling Test [79] RWMOP23 Reactor Network Design [80] RWMOP24 Heat Exchanger Network Design [81] Process, Design, and Synthesis Problems RWMOP25 Process Synthesis Problem [82] RWMOP26 Process Synthesis, and Design Problem [83] RWMOP27 Process Flow Sheeting Problem [84] RWMOP28 Two-Reactor Problem [82] RWMOP29 Process Synthesis Problem [82] Table 7. Cont.…”

Enhanced Jaya Algorithm for Multi-objective Optimisation Problems

“…Multiple population-oriented metaheuristic algorithms were recommended for multi-objective problem (MOP) solving. As MOP goals must simultaneously optimise the conflicting nature of multiple objectives given the absence of one distinct alternative to optimise all collaborative counterparts [1], a set of optimal trade-off alternatives (Pareto) was employed as a solution. Thus, a single and optimal solution is non-existent in this regard.…”