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Non-dominated moth-flame optimization algorithm (NS-MFO) is based on the search technique of moth-flame optimization algorithm (MFO) algorithm and utilizes the elitist non-dominated sorting and crowding distance approach for obtaining different non domination levels and to preserve the diversity among the optimal set of solutions respectively.
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Non-dominated sorting in genetic algorithms-II (NSGA-II) is a popular non-domination based genetic algorithm for solving multi-objective optimization problems.
The ith solution has better rank in a non-dominant sorting procedure ((r_{i} < r_{j})) that means this solution has better non-domination degree comparing with jth solution, 2.
The ith solution has better rank in a non-dominant sorting procedure ((r_{i} < r_{j})) that means this solution has better non-domination degree comparing with jth solution, If ith solution has more crowding distance comparing with jth solution ((d_{i} > d_{j})), when the rank of both solutions are equal.
Then, the Pareto optimal selection of powertrain configuration and its corresponding optimal component parameters are obtained by performance comparison and non-domination sorting.
Several numerical experiments indicate that the proposed algorithm outperforms four compared algorithms in terms of finding a set of well-distributed non-domination solutions.
As the lbests are chosen from the top fronts in a non-domination sorted external archive of reasonably large size, the offspring obtained can be more diverse with good fitness.
The population is now sorted based on non-domination.
The population is sorted based on the non-domination scheme.
In these cases, we use a non-domination rank for each solution, so that each solution is rated based on non-domination.
This procedure was repeated for every solution creating different groups or non-domination; an integer value called rank was assigned to each non-domination level.
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