Multiobjective Optimization
30 papers with code • 0 benchmarks • 1 datasets
Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
Benchmarks
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Deep Sea Treasure Pareto-Front
Latest papers with no code
Multiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations
Despite the fact that a prototypical version of the IOP has been introduced in the literature before, a deeper analysis focused on the resolution of the problem is needed, in order to determine which is the most appropriate multiobjective method for embedding in the IOP.
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem
Minimum connected dominating set problem is an NP-hard combinatorial optimization problem in graph theory.
Dealing with Structure Constraints in Evolutionary Pareto Set Learning
In this work, we make the first attempt to incorporate the structure constraints into the whole solution set by a single Pareto set model, which can be efficiently learned by a simple evolutionary stochastic optimization method.
qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
Classical evolutionary approaches for multiobjective optimization are quite effective but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles.
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines.
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic.
Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search
In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems.
Efficiently Tackling Million-Dimensional Multiobjective Problems: A Direction Sampling and Fine-Tuning Approach
To sample the most suitable evolutionary directions for different solutions, Thompson sampling is adopted for its effectiveness in recommending from a very large number of items within limited historical evaluations.
Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective
This methodology offers a first attempt to simultaneously measure the performance in approximating the Pareto front and constraint satisfaction.
ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization
In the feasible phase, the tradeoff between diversity and convergence is considered to attain a set of well-converged and well-distributed feasible solutions.
