Multi-objective optimisation using evolutionary algorithms constitutes a powerful computational framework that addresses complex problems involving conflicting objectives. By emulating natural ...

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 ...

In this optimization model, each solution determines the value of C i for all components. Since a solution should contain the sequence of multiple operations of multiple components, it has to be ...

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 ...

Supply chains consist of imperfect humans struggling to make perfect decisions. In the end, though, it all comes down to a ...

This multi-granularity representation enhances global search efficiency and accelerates convergence. MGO outperforms many popular state-of-the-art algorithms in the CEC2013 single-objective ...

Co-optimizing air temperature (AT) and CO2 concentration (CO2) for AIoT systems in controlled environment agriculture (CEA) has raised sustained attentions to balance energy efficiency and crop ...

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 ...