Multiobjective optimization with ϵ-constrained method for solving real-parameter constrained optimization problems

TL;DR: In this article , an effective evolutionary algorithm with a dynamic population size (DPSEA) was proposed to balance exploration and exploitation, in which the population size of DPSEA decreases continually as generation increases.

TL;DR: The experimental results show that the proposed density-enhanced multiobjective evolutionary approach to solve economic dispatch problem performs better than or at least competitive to the state-of-the-art algorithms, especially on large-scale ED problems.

TL;DR: A novel method has been presented in order to minimize production cost and emission of the steam power plants in short term periods and showed that the proposed method can be used in short-term decision making ofSteam power plants which will be absolutely effective in long-term emission target oriented strategies.

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.

TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.

TL;DR: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented and it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach.

TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.