A hybrid COA-DEA method for solving multi-objective problems

TL;DR

This paper introduces a hybrid method combining the Cuckoo Optimization Algorithm with Data Envelopment Analysis to effectively solve multi-objective problems, improving solution speed and accuracy.

Contribution

It develops a novel hybrid COA-DEA algorithm based on CCR model and output-oriented approach for multi-objective optimization.

Findings

Enhanced speed in finding solutions

Improved accuracy of Pareto frontiers

Effective handling of multi-objective problems

Abstract

The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented approach of it. The selection criterion is higher efficiency for next iteration of the proposed hybrid method. So the profit function of the COA is replaced by the efficiency value that is obtained from DEA. This algorithm is compared with other methods using some test problems. The results shows using COA and DEA approach for solving multi-objective problems increases the speed and the accuracy of the generated solutions.