This chapter focuses on problems of large scale multi‐objective optimization problems (LSMOPs) and large scale many‐objective optimization problems. It refers to research adopting evolutionary ...

Multi-agent optimization problems with many objective functions have drawn much interest over the past two decades. Many works on the subject minimize the sum of objective functions, which implicitly ...

Multi-objective optimization (MOO) is a critical technique in AI that enables optimizing multiple competing objectives simultaneously. Rather than a single optimal solution, MOO produces a Pareto ...

In the algorithm performance test, it is better than MOSSA algorithm, NSGAII algorithm and MOPSO algorithm, and can better solve the multi-objective optimization problem. 2) The multi-objective ...

A multi-objective optimal power flow (MOPF) algorithm is proposed to minimize two optimization targets, i.e., overall active power loss and generation costs of the system. To increase the degree of ...

The double dogleg optimization technique works well for medium to moderately large optimization problems where the objective function and the gradient are much faster to compute than the Hessian. The ...

Nelder-Mead Simplex Optimization (NMSIMP) The Nelder-Mead simplex method does not use any derivatives and does not assume that the objective function has continuous derivatives. The objective function ...