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Process knowledge-guided autonomous evolutionary optimization for constrained multi-objective problems
Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.
Essien, Mmekutmfon Sunday(University of Pretoria, 2012)
The aim and objective of this research is to formulate and solve a multi-objective optimization problem
for the optimal placement of multiple rows and multiple columns of fixed flat-plate solar collectors
in a field. ...
This paper discusses the maintenance plan optimization problem for the energy efficiency purpose in thebuilding energy efficiency retrofitting context. A subproblem namely the Building Retrofitted FacilitiesCorrective ...
Yekoladio, Peni Junior(University of Pretoria, 2013)
The present work addresses the thermodynamic optimization of small binary-cycle geothermal power plants. The optimization process and entropy generation minimization analysis were performed to minimize the overall exergy ...