By Lam Thu Bui, Sameer Alam
Multi-objective optimization (MO) is a fast-developing box in computational intelligence learn. Giving selection makers extra strategies to choose between utilizing a few post-analysis choice details, there are many aggressive MO recommendations with an more and more huge variety of MO real-world functions. Multi-Objective Optimization in Computational Intelligence: thought and perform explores the theoretical, in addition to empirical, functionality of MOs on quite a lot of optimization matters together with combinatorial, real-valued, dynamic, and noisy difficulties. This booklet offers students, lecturers, and practitioners with a basic, entire selection of learn on multi-objective optimization strategies, functions, and practices.
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Extra resources for Multi-objective optimization in computational intelligence: theory and practice
Zitzler, E. (2003). Pisa: A platform and programming language independent interface for search algorithms. Evolutionary multi-criterion optimisation. Lecture notes in computer science, (Vol. 2632, pp. 494-508). Springer. Branke, J. (2002). Evolutionary optimization in dynamic environments. Massachusetts: Kluwer Academic Publishers. Bui, L. T. (2007). The role of communication messages and explicit niching in distributed evolutionary multi-objective optimization. PhD Thesis, University of New South Wales.
The main feature of NSGA-II lies in its elitism-preservation operation. Note that NSGA-II does not use an explicit archive; a population is An Introduction to Multi-Objective Optimization used to store both elitist and non-elitist solutions for the next generation. However, for consistency, it is still considered as an archive. Firstly, the archive size is set equal to the initial population size. The current archive is then determined based on the combination of the current population and the previous archive.
Although MOEAs are different from each other, the common steps of these algorithms can be summarized as next. Note that Steps 2 and 5 are used for elitism approaches that will be summarized in the next subsection. • • • • • • Step 1: Initialize a population P Step 2: (optional): Select elitist solutions from P to create/update an external set FP (For non-elitism algorithms, FP is empty). Step 3: Create mating pool from one or both of P and FP Step 4: Perform reproduction based on the pool to create the next generation P Step 5: Possibly combine FP into P Step 6: Go to Step 2 if the termination condition is not satisfied.
Multi-objective optimization in computational intelligence: theory and practice by Lam Thu Bui, Sameer Alam