Evolutionary algorithms
This data can be used for all the experiments related to the Mars rovers, as this data are accurate and used in a new algorithm called Limited Weighted Sum Genetic Algorithm for Multi-Objectives optimisation (LWSGA-MO).
The Mars exploration rover dataset is created in two steps: the data generation, and the data processing.
1) The data generation was done by Unity, and the code was written by C# scripts.
2) The data processing was done by RStudio and R.
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<p>Numerical comparison serves as a major tool in evaluating the performance of optimization algorithms, especially nondeterministic algorithms, but existing methods may suffer from a ‘cycle ranking’ paradox and/or a ‘survival of the nonfittest paradox. This paper searches for paradox-free data analysis methods for numerical comparison. It is discovered that a class of sufficient conditions exist for designing paradox-free analysis. Rigorous modeling and deduction are applied to a class of profile methods employing a filter.
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In large-scale multi-objective optimization, as the decision space's dimensionality increases, evolutionary algorithms can easily fall into an optimal local state. Therefore, how to prevent the algorithm from falling into a local optimum and quickly converge to the Pareto front is a particularly challenging problem. In order to solve the problem, this paper proposes a grid-based fuzzy evolution large-scale multi-objective optimization framework, which divides the entire evolution process into two main stages: fuzzy evolution and precise evolution.
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