customhys

Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.

Detailed information about this framework can be found in [1, 2]. Plus, the code for each module is well-documented.

🛠 Requirements:

• Check the requirements.txt file.
• For Apple Silicon, one may need to install TensorFlow via conda, such as:

conda install -c apple tensorflow-deps

Further information can be found at Install TensorFlow on Mac M1/M2 with GPU support by D. Ganzaroli.

🧰 Modules

The modules that comprise this framework depend on some basic Python packages, and they liaise with each other. The module dependency diagram is presented as follows:

NOTE: Each module is briefly described below. If you require further information, please check the corresponding source code.

🤯 Problems (benchmark functions)

This module includes several benchmark functions as classes to be solved by using optimisation techniques. The class structure is based on Keita Tomochika's repository optimization-evaluation.

Source: benchmark_func.py

👯‍♂️ Population

This module contains the class Population. A Population object corresponds to a set of agents or individuals within a problem domain. These agents themselves do not explore the function landscape, but they know when to update their position according to a selection procedure.

Source: population.py

🦾 Search Operators (low-level heuristics)

This module has a collection of search operators (simple heuristics) extracted from several well-known metaheuristics in the literature. Such operators work over a population, i.e., modify the individuals' positions.

Source: operators.py

🤖 Metaheuristic (mid-level heuristic)

This module contains the Metaheuristic class. A metaheuristic object implements a set of search operators to guide a population in a search procedure within an optimisation problem.

Source: metaheuristic.py

👽 Hyper-heuristic (high-level heuristic)

This module contains the Hyperheuristic class. Similar to the Metaheuristic class, but in this case, a collection of search operators is required. A hyper-heuristic object searches within the heuristic space to find the sequence that builds the best metaheuristic for a specific problem.

Source: hyperheuristic.py

🏭 Experiment

This module contains the Experiment class. An experiment object can run several hyper-heuristic procedures for a list of optimisation problems.

Source: experiment.py

🗜️ Tools

This module contains several functions and methods utilised by many modules in this package.

Source: tools.py

🧠 Machine Learning

This module contains the implementation of Machine Learning models, which can power a hyper-heuristic model from this framework. In particular, it is implemented a wrapper for a Neural Network model from Tensorflow. Also, contains auxiliary data structures which process samples of sequences to generate training data for Machine Learning models.

Source: machine_learning.py

💾 Data Structure

The experiments are saved in JSON files. The data structure of a saved file follows a particular scheme described below.

Expand structure

data_frame = {dict: N}
|-- 'problem' = {list: N}
|  |-- 0 = {str}
:  :
|-- 'dimensions' = {list: N}
|  |-- 0 = {int}
:  :
|-- 'results' = {list: N}
|  |-- 0 = {dict: 6}
|  |  |-- 'iteration' = {list: M}
|  |  |  |-- 0 = {int}
:  :  :  :
|  |  |-- 'time' = {list: M}
|  |  |  |-- 0 = {float}
:  :  :  :
|  |  |-- 'performance' = {list: M}
|  |  |  |-- 0 = {float}
:  :  :  :
|  |  |-- 'encoded_solution' = {list: M}
|  |  |  |-- 0 = {int}
:  :  :  :
|  |  |-- 'solution' = {list: M}
|  |  |  |-- 0 = {list: C}
|  |  |  |  |-- 0 = {list: 3}
|  |  |  |  |  |-- search_operator_structure
:  :  :  :  :  :
|  |  |-- 'details' = {list: M}
|  |  |  |-- 0 = {dict: 4}
|  |  |  |  |-- 'fitness' = {list: R}
|  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :
|  |  |  |  |-- 'positions' = {list: R}
|  |  |  |  |  |-- 0 = {list: D}
|  |  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :  :
|  |  |  |  |-- 'historical' = {list: R}
|  |  |  |  |  |-- 0 = {dict: 5}
|  |  |  |  |  |  |-- 'fitness' = {list: I}
|  |  |  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :  :  :
|  |  |  |  |  |  |-- 'positions' = {list: I}
|  |  |  |  |  |  |  |-- 0 = {list: D}
|  |  |  |  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :  :  :  :
|  |  |  |  |  |  |-- 'centroid' = {list: I}
|  |  |  |  |  |  |  |-- 0 = {list: D}
|  |  |  |  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :  :  :  :
|  |  |  |  |  |  |-- 'radius' = {list: I}
|  |  |  |  |  |  |  |-- 0 = {float}
:  :  :  :  :  :  :  :
|  |  |  |  |  |  |-- 'stagnation' = {list: I}
|  |  |  |  |  |  |  |-- 0 = {int}
:  :  :  :  :  :  :  :
|  |  |  |  |-- 'statistics' = {dict: 10}
|  |  |  |  |  |-- 'nob' = {int}
|  |  |  |  |  |-- 'Min' = {float}
|  |  |  |  |  |-- 'Max' = {float}
|  |  |  |  |  |-- 'Avg' = {float}
|  |  |  |  |  |-- 'Std' = {float}
|  |  |  |  |  |-- 'Skw' = {float}
|  |  |  |  |  |-- 'Kur' = {float}
|  |  |  |  |  |-- 'IQR' = {float}
|  |  |  |  |  |-- 'Med' = {float}
|  |  |  |  |  |-- 'MAD' = {float}
:  :  :  :  :  :

where:

• N is the number of files within data_files folder
• M is the number of hyper-heuristic iterations (metaheuristic candidates)
• C is the number of search operators in the metaheuristic (cardinality)
• P is the number of control parameters for each search operator
• R is the number of repetitions performed for each metaheuristic candidate
• D is the dimensionality of the problem tackled by the metaheuristic candidate
• I is the number of iterations performed by the metaheuristic candidate
• search_operator_structure corresponds to [operator_name = {str}, control_parameters = {dict: P}, selector = {str}]

🏗️ Work-in-Progress

The following modules are available, but they may not work. They are currently under development.

🌡️ Characterisation

This module intends to provide metrics for characterising the benchmark functions.

Source: characterisation.py

📊 Visualisation

This module intends to provide several tools for plotting results from the experiments.

Source: visualisation.py

References

Seminal Papers

The seminal papers that describe the framework's theoretical background and software implementation are:

1. J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marín, and Y. Shi, CUSTOMHyS: Customising Optimisation Metaheuristics via Hyper-heuristic Search, SoftwareX, vol. 12, p. 100628, 2020.
2. J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, Y. Shi, H. Terashima-Marín, and N. Pillay, Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems, Mathematics, vol. 8, no. 11, p. 2046, Nov. 2020.
3. J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Connat-Pablos, and H. Terashima-Marín, A Primary Study on Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation. 2020 IEEE Congress on Evolutionary Computation (CEC), 2020.
4. J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, S. E. Conant-Pablos, H. Terashima-Marín, H., and Y. Shi. Hyper-Heuristics to Customise Metaheuristics for Continuous Optimisation, Swarm and Evolutionary Computation, 100935.

Published Journal Papers

These are the journal articles that have been published using this framework:

1. G. C. Duque-Gimenez, D. F. Zambrano-Gutierrez, M. Rodriguez-Nieto, J. L. Menchaca, J. M. Cruz-Duarte, D. G. Zárate-Triviño, J. G. Avina-Cervantes, J. C. Ortiz-Bayliss, Viscoelastic Characterization of the Human Osteosarcoma Cancer Cell Line MG-63 Using a Fractional-Order Zener Model Through Automated Algorithm Design and Configuration, Nature Scientific Reports, 15, 31436, 2025.
2. D. F. Zambrano-Gutierrez, J. M. Cruz-Duarte, H. Castañeda, J. G. Avina-Cervantes, Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters, Swarm and Evolutionary Computation, 12(23), 370, 2024.
3. J. M. Tapia-Avitia, J. M. Cruz‐Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marín, and N. Pillay, Analysing Hyper-Heuristics based on Neural Networks for the Automatic Design of Population-based Metaheuristics in Continuous Optimisation Problems, Swarm and Evolutionary Computation, 89, 101616, 2024.
4. D. F. Zambrano-Gutierrez, G. H. Valencia-Rivera, J. G. Avina-Cervantes, I. Amaya, and J. M. Cruz-Duarte, Designing Heuristic-Based Tuners for Fractional-Order PID Controllers in Automatic Voltage Regulator Systems Using a Hyper-heuristic Approach, Fractal Fract, 2024.
5. D. F. Zambrano-Gutierrez, J. M. Cruz-Duarte, J. G. Avina-Cervantes, J. C. Ortiz-Bayliss, J. J. Yanez-Borjas, and I. Amaya, Automatic Design of Metaheuristics for Practical Engineering Applications, IEEE Access., vol. 11, pp. 7262-7276, 2023.
6. J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, and N. Pillay, Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics, Appl. Sci., vol. 11, no. 12, p. 5620, 2021.

Presented Conference Papers

These are the conference articles that have been presented using this framework:

1. D. F. Zambrano-Gutierrez, J. Ramos-Frutos, O. Ramos-Soto, J. G. Avina-Cervantes, D. Oliva, J. M. Cruz-Duarte, Automated Tailoring of Heuristic-Based Renyi’s Entropy Maximizers for Efficient Melanoma Segmentation, 2025 IEEE Symposium on Computational Intelligence in Image, Signal Processing and Synthetic Media (CISM), 2024.
2. D. F. Zambrano-Gutierrez, J. M. Cruz-Duarte, J. C. Ortiz-Bayliss, I. Amaya, and J. G. Avina-Cervantes, Beyond Traditional Tuning: Unveiling Metaheuristic Operator Trends in PID Control Tuning for Automatic Voltage Regulation, 2024 IEEE Congress on Evolutionary Computation (CEC), 2024.
3. G. Pérez-Espinosa, J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marín, and N. Pillay, Tailoring Metaheuristics for Designing Thermodynamic-Optimal Cooling Devices for Microelectronic Thermal Management Applications, 2024 IEEE Congress on Evolutionary Computation (CEC), 2024.
4. D. Acosta-Ugalde, J. M. Cruz-Duarte, S. E. Conant-Pablos, and J. G. Falcón-Cardona, Beyond 'Novel' Metaphor-based Metaheuristics: An Interactive Algorithm Design Software, 2024 IEEE Congress on Evolutionary Computation (CEC), 2024.
5. D. F. Zambrano-Gutierrez, A. C. Molina-Porras, J. G. Avina-Cervantes, R. Correa, and J. M. Cruz-Duarte, Designing Heuristic-Based Tuners for PID Controllers in Automatic Voltage Regulator Systems Using an Automated Hyper-Heuristic Approach, 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 1263-1268.
6. D. F. Zambrano-Gutierrez, A. C. Molina-Porras, E. Ovalle-Magallanes, I. Amaya, J. C. Ortiz-Bayliss, J. G. Avina-Cervantes, and J. M. Cruz-Duarte, SIGNRL: A Population-Based Reinforcement Learning Method for Continuous Control, 2023 IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, 2023, pp. 1443-1448.
7. D. F. Zambrano-Gutierrez, J. M. Cruz-Duarte, and H. Castañeda, Automatic Hyper-Heuristic to Generate Heuristic-based Adaptive Sliding Mode Controller Tuners for Buck-Boost Converters, in The Genetic and Evolutionary Computation Conference (GECCO), 2023, pp. 1-8. Nominated to Best Paper Award
8. J. M. Tapia-Avitia, J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, H. Terashima-Marin, and N. Pillay. A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.
9. J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022.
10. J. M. Cruz-Duarte, I. Amaya, J. C. Ortiz-Bayliss, N. Pillay. Automated Design of Unfolded Metaheuristics and the Effect of Population Size. 2021 IEEE Congress on Evolutionary Computation (CEC), 1155–1162, 2021.

Former Sponsors

This project was born thanks to the initial support of the institutions listed below from 2019 to 2021. They are no longer maintaining it or involved in any form, and perhaps they don't even know they were part of it. Now, it is an independent project maintained by some volunteers, including its creator, who appreciate the contribution of those men in suits who once approved that boring budget line that allowed us, you and me, to share these words.