Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations

Multi-Objective Genetic Algorithm and Simulated Annealing with the Jumping Gene Adaptations 

Manojkumar Ramteke and Santosh K. Gupta Department of Chemical Engineering, 

Keywords Genetic Algorithm, Simulated Annealing, Jumping Gene. 

As discussed in Chapter 1, most real-world engineering problems require the simultaneous optimization of several objectives (multi-objective optimization, MOO) that cannot be compared easily with one another, i.e., are non-commensurate. These cannot be combined into a single, meaningful scalar objective function. A simple, two-objective example, involving two decision (n = 2) variables, x (= [xi, X2, is 

Very popular and robust techniques like genetic algorithm (GA) and simulated annealing (SA) are used to solve such problems. The multiobjective forms of these techniques, e.g., NSGA-II (Deb et al., 2002) and MOSA (Suppapitnarm et al., 2000), are quite commonly used these days. These algorithms often require large amounts of computational (CPU) time. Any adaptation to speed up the solution procedure is, thus. 

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