Everything about Multi-objective optimization
Mathematical concept
In multiple-criteria decision making, multi-objective optimization or Pareto optimization is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that 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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Multi-objective optimizationA 5-minute orientation
- Skim a recent paper: Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization ProblemsInfluential work since 2022
- Pick up a classic book: Optimization in Engineering SciencesThe most-reprinted book on the subject
- Read the landmark paper: Guidance in evolutionary multi-objective optimizationCited 276 times
- Go sideways: Pareto frontierA closely related topic
- Go sideways: Fitness functionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Guidance in evolutionary multi-objective optimization
Evolutionary Large-Scale Multi-Objective Optimization: A Survey
A Hybrid Framework for Evolutionary Multi-Objective Optimization
Research on Evolutionary Multi-Objective Optimization Algorithms
Robustness in multi-objective optimization using evolutionary algorithms
Multi-objective topology optimization using evolutionary algorithms
Evolutionary multi-objective portfolio optimization in practical context
An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints
🆕 Influential since 2022
Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems
Evolutionary constrained multi-objective optimization: a review
Intersection Signal Timing Optimization: A Multi-Objective Evolutionary Algorithm
Evolutionary Computation for Sparse Multi-Objective Optimization: A Survey
Multi-objective evolutionary optimization for multi-period heat exchanger network retrofit
📚 Books worth reading
Optimization in Engineering SciencesPierre Borne, Abdelkader El Kamel · 2012
Genetic ProgrammingKrzysztof Krawiec · 2013
Intelligent Information and Database SystemsAli Selamat · 2013- 📕Evolutionary Large-Scale Multi-Objective Optimization and ApplicationsXingyi Zhang, Ran Cheng · 2024
Neuro-fuzzy and soft computingJyh-Shing Roger Jang, Chuen-Tsai Sun · 1996
Recent Advances in Evolutionary Multi-objective OptimizationSlim Bechikh, Rituparna Datta · 2016
Evolutionary multi-objective optimization in uncertain environmentsChi-Keong Goh · 2009- 📕Multi-objective optimization using evolutionary algorithmsKalyanmoy Deb · 2001
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Pareto frontier In multi-objective optimization, the Pareto frontier (also called the Pareto front, Pareto set, Pareto boundary or Pareto curve) is the set of all Pareto…
- Fitness function A fitness function is a particular type of objective or cost function that is used to summarize, as a single figure of merit, how close a given candidate…
- Evolutionary multi-objective optimization Evolutionary multi-objective optimization (EMO), also called evolutionary multi-criterion optimization, is the application of evolutionary algorithms to…
- Hypervolume indicator The hypervolume indicator is a performance indicator used in multi-objective optimization to measure the quality of a finite approximation of the Pareto…
- Evolutionary multimodal optimization In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal)…
- Bayesian optimization Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is…
- Test functions for optimization In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence…
- Particle swarm optimization In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a population…