Real World Benchmarks

This repository contains a collection of multi- and many-objective optimization problems with real-world applications for benchmarking multiobjective evolutionary algorithms (MOEAs).

Installation

These codes are intended for use with the MOEA Framework. Follow the steps below to setup these real-world benchmark problems:

Prerequisites

Ensure your system has the following software installed:

1. Java 17+
2. Maven
3. GNU Make
4. GNU C/C++ compilers (gcc and g++)

Setup MOEA Framework

Download the latest MOEA Framework binaries or source code from http://moeaframework.org/ and extract the archive to a folder on your computer. We will refer to this as folder as ${MOEAFRAMEWORK_ROOT} in the following steps.

Install Real-World Benchmark Library

Download the latest version of the real-world benchmarks JAR file from the releases page and place it in the ${MOEAFRAMEWORK_ROOT}/lib folder.

Compile Benchmark Problems

Several of the benchmark problems must be compiled before use. If using Windows, we include compiled executables for each release version (see native-windows.zip). Otherwise, to compile the executables, run:

1. Clone this repository - git clone https://github.com/MOEAFramework/RealWorldBenchmarks.git
2. Run make -C native
3. Copy or link the native/ folder into your MOEA Framework directory using either:
   • Option 1 - Copy the entire directory with cp -R native/ ${MOEAFRAMEWORK_ROOT}/native
   • Option 2 - Create a symbolic link with ln -s $(realpath -s native/) ${MOEAFRAMEWORK_ROOT}/native

Maven

Alternatively, if you want to include these benchmark problems in a Maven project, add the following dependency to your pom.xml. Please note that you will still need to compile the native executables separately.

<dependency>
    <groupId>org.moeaframework</groupId>
    <artifactId>real-world-benchmarks</artifactId>
    <version>1.1.0</version>
</dependency>

Usage

To run one of these real-world benchmark problems, you can then either directly construct the problem:

Problem problem = new GAA();

NSGAII algorithm = new NSGAII(problem);
algorithm.run(10000);

NondominatedPopulation result = algorithm.getResult();

or reference it by name if using the Executor:

NondominatedPopulation result = new Executor()
   .withProblem("GAA")
   .withAlgorithm("NSGAII")
   .withMaxEvaluations(10000)
   .run();

Available Benchmarks

The following benchmark problems are available:

Problem	Problem Name	Variables	Objectives	Constraints	References
General Aviation Aircraft	GAA	27	10	1	[1]-[4]
HBV Rainfall-Runoff Model Calibration	HBV	14	4	0	[5]
Radar Waveform Optimization	Radar	8	9	0	[6]
Car Side Impact	CarSideImpact	7	3	10	[7]
Water Supply Portfolio Planning	LRGV	8	5	4	[5], [8]
Lake Pollution Control Policy	LakeProblem	100	4	1	[9]-[11]
Electric Motor Product Family	ElectricMotor	80	20	60	[12]

In addition, this repository contains twelve bi-objective water distribution system (WDS) design problems [13] ranging from 8 to 567 decision variables:

Problem	Problem Name	Variables	Objectives	Constraints
Two-reservior Network (TRN)	WDS(TRN)	8	2	1
Two-loop Network (TLN)	WDS(TLN)	8	2	1
BakRyan Network (BAK)	WDS(BAK)	9	2	1
New York Tunnel Network (NYT)	WDS(NYT)	21	2	1
Blacksburg Network (BLA)	WDS(BLA)	23	2	1
Hanoi Network (HAN)	WDS(HAN)	34	2	1
GoYang Network (GOY)	WDS(GOY)	30	2	1
Fossolo Network (FOS)	WDS(FOS)	58	2	1
Pescara Network (PES)	WDS(PES)	99	2	1
Modena Network (MOD)	WDS(MOD)	317	2	1
Belerma Irrigation Network (BIN)	WDS(BIN)	454	2	1
Exeter Network (EXN)	WDS(EXN)	567	2	1

Additional information for specific problems can be found in the cited papers as well as the README files and other documentation for each problem.

License

Most of the software contained in this repository is copyright by the respective authors who developed each benchmark problem. Please cite these original works if using any of the benchmark problems.

References

1. T. W. Simpson, W. Chen, J. K. Allen, and F. Mistree (1996). "Conceptual design of a family of products through the use of the robust concept exploration method." In 6th AIAA/USAF/NASA/ ISSMO Symposium on Multidiciplinary Analysis and Optimization, vol. 2, pp. 1535-1545. (Link)

2. T. W. Simpson, B. S. D'Souza (2004). "Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm." Concurrent Engineering: Research and Applications, vol. 12, no. 2, pp. 119-130. (Link)

3. R. Shah, P. M. Reed, and T. W. Simpson (2011). "Many-objective evolutionary optimization and visual analytics for product family design." Multiobjective Evolutionary Optimisation for Product Design and Manufacturing, Springer, London, pp. 137-159. (Link)

4. D. Hadka, P. M. Reed, and T. W. Simpson (2012). "Diagnostic Assessment of the Borg MOEA on Many-Objective Product Family Design Problems." WCCI 2012 World Congress on Computational Intelligence, Congress on Evolutionary Computation, Brisbane, Australia, pp. 986-995. (Link)

5. P.M. Reed, D. Hadka, J.D. Herman, J.R. Kasprzyk, J.B. Kollat (2013). "Evolutionary multiobjective optimization in water resources: The past, present, and future." Advances in Water Resources, 51:438-456. (Link)

6. E. J. Hughes (2007). "Radar Waveform Optimisation as a Many-Objective Application Benchmark." Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, 4403:700-714. (Link)

7. J. Jain and K. Deb. "An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach." IEEE Transactions on Evolutionary Computation, 18(4):602-622, 2014. (Link)

8. J. R. Kasprzyk, P. M. Reed, B. R. Kirsch, and G. W. Characklis (2012). "Many-Objective de Novo Water Supply Portfolio Planning Under Deep Uncertainty." Environmental Modelling & Software, 34:87-104. (Link)

9. R. Singh, P. M. Reed, and K. Keller (2015). "Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response", Ecology and Society v20, No.3, 12, doi:10.5751/ES-07687-200312. (Link)

10. V. Ward, R. Singh, P. M. Reed, and K. Keller (2015). "Confronting Tipping Points: Can Multi-objective Evolutionary Algorithms Discover Pollution Control Tradeoffs Given Environmental Thresholds?", Environmental Modelling & Software, v73, 27-43. (Link)

11. S. R. Carpenter, D. Ludwig, and W. A. Brock (1999). "Management of eutrophication for lakes subject to potentially irreversible change." Ecological Applications 9:751-771. (Link)

12. T. W. Simpson, J. R. A. Maier, and F. Mistree (2001). "Product platform design: method and application." Res Eng Design, 13:2-22.

13. Q. Wang, M. Guidolin, D. Savic, and Z. Kapelan (2015). "Two-Objective Design of Benchmark Problems of a Water Distribution System via MOEAs: Towards the Best-Known Approximation of the True Pareto Front." Journal of Water Resources Planning and Management, 141(3), 04014060. (Link)