A Visualizable Test Problem Generator for Many-Objective Optimization
TL;DR: In this paper , the authors propose an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features, some of which are newly introduced here or form extensions of existing features.
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Abstract: Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distance-based many-objective optimization test problems have been developed to facilitate visualization of search behavior in a 2-D design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors’ previous work has advanced this research further by providing a problem generator to automatically create user-defined problem instances featuring any combination of these problem features as well as newly introduced ones, such as landscape discontinuities, varying objective ranges, and neutrality. This work makes a number of additional contributions including the proposal of an enhanced, open-source feature-rich problem generator that can create user-defined problem instances exhibiting a range of problem features—some of which are newly introduced here or form extensions of existing features. A comprehensive validation of the problem generator is also provided using popular multiobjective optimization algorithms, and some problem generator settings to create instances exhibiting different challenges for an optimizer are identified.
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Citations
SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound
TL;DR: SonOpt as discussed by the authors is a tool for the sonification of bi-objective population-based optimisation algorithms, which can provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set.
Evolutionary Many‐objective Optimization: Difficulties, Approaches, and Discussions
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TL;DR: Various difficulties in evolutionary many‐objective optimization are explained, representative approaches are reviewed, and their effects and limitations are discussed.
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
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TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
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