A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites
Z Wang, W Zhang, Y Guo, M Han, B Wan… - Applied Soft Computing, 2023 - Elsevier
Multi-objective optimization problems (MOPs) that widely exist in real world concern all
optimal solutions compromised among multiple objectives. Chicken swarm optimization …
optimal solutions compromised among multiple objectives. Chicken swarm optimization …
Quality indicators for preference-based evolutionary multi-objective optimization using a reference point: A review and analysis
Some quality indicators have been proposed for benchmarking preference-based
evolutionary multi-objective optimization algorithms using a reference point. Although a …
evolutionary multi-objective optimization algorithms using a reference point. Although a …
Finding top-k solutions for the decision-maker in multiobjective optimization
Multiobjective optimization problems (MOPs) are the optimization problem with multiple
conflicting objectives. Generally, an optimization algorithm can find a large number of …
conflicting objectives. Generally, an optimization algorithm can find a large number of …
On multiobjective knapsack problems with multiple decision makers
Many real-world optimization problems require optimizing multiple conflicting objectives
simultaneously, and such problems are called multiobjective optimization problems (MOPs) …
simultaneously, and such problems are called multiobjective optimization problems (MOPs) …
MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems
Many-objective optimization (MaO) deals with a large number of conflicting objectives in
optimization problems to acquire a reliable set of appropriate non-dominated solutions near …
optimization problems to acquire a reliable set of appropriate non-dominated solutions near …
An Updated Performance Metric for Preference-Based Evolutionary Multi-Objective Optimization Algorithms
Evolutionary multi-objective optimization (EMO) algorithms are widely used to solve
problems involving multiple conflicting objectives. In general, these problems result in a well …
problems involving multiple conflicting objectives. In general, these problems result in a well …
Interpretable self-organizing map assisted interactive multi-criteria decision-making following Pareto-Race
The problem-solving task of the multi-criteria decision-making (MCDM) approach involves
decision makers'(DMs') interaction by incorporating their preferences to arrive at one or …
decision makers'(DMs') interaction by incorporating their preferences to arrive at one or …
Evolutionary approach to multiparty multiobjective optimization problems with common pareto optimal solutions
Some real-world optimization problems involve multiple decision makers holding different
positions, each of whom has multiple conflicting objectives. These problems are defined as …
positions, each of whom has multiple conflicting objectives. These problems are defined as …
A novel dynamic reference point model for preference-based evolutionary multiobjective optimization
In the field of preference-based evolutionary multiobjective optimization, optimization
algorithms are required to search for the Pareto optimal solutions preferred by the decision …
algorithms are required to search for the Pareto optimal solutions preferred by the decision …
DIP-MOEA: A double-grid interactive preference based multi-objective evolutionary algorithm for formalizing preferences of decision makers
L Zhao, B Wang, X Jiang, Y Lu, Y Hu - Frontiers of Information Technology …, 2022 - Springer
The final solution set given by almost all existing preference-based multi-objective
evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers' …
evolutionary algorithms (MOEAs) lies a certain distance away from the decision makers' …