Multi-objective clustering basically aims at placing similar objects into the same groups based on some conflicting objectives, which substantially supports the use of game theory to come to a resolution.
The optimizing design variables are based on two mutually conflicting objectives, involving maximizing the isolation efficiency as well as isolator displacement.
An integer programming formulation is first established based on connection network with three conflicting objectives, where the first objective minimizes the total deviation from original flight schedules, the second objective minimizes the maximal flight delay time, and the third objective minimizes the number of aircraft actually attended in swapping.
The framework is based on geometric, economic and electrochemical models and focuses on evaluating the effect of uncertainty in operating parameters on three conflicting objectives: electricity efficiency, SOFC current density and capital cost of system.
This paper presents an approach based on fuzzy logic that combines the conflicting objectives into a single optimization function.
However, the existing research on objective reduction focuses on global redundancy between objectives [144, 145, 146], but little work has been conducted on locally conflicting objectives.
Bandyopadhyay and Mukherjee [12] developed an algorithm for MaOPs that periodically orders objectives based on the correlation and selects a subset of conflicting objectives.
Since in the multi-level decentralized decision companies the individual set of decision variables is often controlled by each level which have their own, often mutually conflicting, objectives the evaluation of banking chain performance is based on multi-perspective.
To solve the highly constrained environmental/economic dispatch problem involving conflicting objectives, this paper presents a hybrid multi-objective optimization algorithm based on particle swarm optimization (PSO) and differential evolution (DE).
