3.3.2. Multi-Objective EHO

Jaiprakash et al. [124] presented a multi-objective clustering EHO (MOEHO) to solve multi-objective optimization problems. Comparative results revealed that MOEHO provided superior performance compared with (fast and elitist multiobjective genetic algorithm) NSGA-II and MOPSO in eight cases. In addition, MOEHO was used to cluster the activities of human models. The results showed that MOEHO succeeded in eight out of five case studies.

Meena et al. [125] presented improved multi-objective EHO (IMOEHO) to solve distribution system optimization problems. In IMOEHO, two techniques (order of preference by similarity to the ideal solution technique and improved EHO technique) were combined. The IMOEHO method was implemented in three benchmark test distribution systems. It was concluded that the IMOEHO method was very effective for optimizing multi-objective complex optimization problems.
