Mastering Multi-Objective Optimization Techniques for Industrial Engineering
Explore the world of multi-objective optimization in industrial engineering through heuristic and metaheuristic approaches. Learn about aggregated weighted sum methods, Pareto optimization, and popular algorithms like MOGA and NSGA-II. Discover how to find trade-offs efficiently in smart industry applications for sustainable solutions.
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Presentation Transcript
Advanced Optimization: Techniques and Industrial Applications Module 2: Heuristics and Metaheuristics Curriculum Development of Master s Degree Program in Industrial Engineering for Thailand Sustainable Smart Industry
Session 2.4: Multiobjective optimization Curriculum Development of Master s Degree Program in Industrial Engineering for Thailand Sustainable Smart Industry
Multi-objective Optimization Optimization Problem Single-objective Multi-objective Pareto Aggregated Weighted Sum
Multi-objective Optimization Aggregated weighted sum Different objectives are assigned different weights and combined into a single objective Optimize f(x) = w1.f1(x) + w2.f2(x) + + wk.fk(x) where wkis the weight assigned to the objective k
Multi-objective Optimization Aggregated weighted sum Easy but Requires pre-determine weights for each objective function Yields only one single solution at a time To be more objective, this approach needs to be run several times in order to find sets of solutions corresponding to varying weights, and as the result, these approaches are highly time consuming
Multi-objective Optimization Pareto Approach Weight-free method Provide trade-offs in a single run without prejudice
Multi-objective Optimization f2 y f2(y) f2(x) x Non- dominated front f1 f1(y) f1(x) x is considered to dominate y (denote x < y) if and only if fi(x) fi(y) for i = 1, 2, , k and j =1, 2, , k | fj(x) < fj(y). For the case that neither x < y nor y < x, x and y are called non-dominated solutions or trade-off solutions
Metaheuristics for Multi-objective Optimization Multi-objective Genetic Algorithm MOGA (Tadahiko Murata and Hisao Ishibuchi. 1995. MOGA: Multi-Objective Genetic Algorithms . NSGA II (Kalyanmoy Deb, Samir Agrawal, Amrit Pratab, and T. Meyarivan. 2002 A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II ) Multi-objective Particle Swarm Optimization MOPSO (C.A. Coello and M.S Lechuga. 2002. MOPSO: a proposal for multiple objective particle swarm optimization ) TV-MOPSO (Tripathi, P. K., Bandyopadhyay, S., and Pal, S. K. 2007. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients . CCS-MOPSO (Kaveh, A. andLaknejadi, K. 2011. A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization . MOLS-MOPSO (Xu, G., Yang, Y., Liu, B.-B., Xu, Y., and Wu, A. 2015. An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search. ETC.
Workshop Presentation of Metaheuristic Applications from Literature Review
