Module org.cicirello.chips_n_salsa

Chips-n-Salsa - A Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms

Copyright © 2002-2022 Vincent A. Cicirello.

DOI:10.21105/joss.02448 Maven Central GitHub release (latest by date) GitHub Repository GNU General Public License Version 3 (GPLv3)

About the Chips-n-Salsa Library

Chips-n-Salsa is a Java library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms. The Chips-n-Salsa library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. The library includes several built-in representations for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of a very large variety of mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library.

Chips-n-Salsa is customizable in that it makes extensive use of generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), and classes that support running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt run length.

The source code repository is hosted on GitHub, and includes the source code and example programs that use the library. The source code is licensed under the GNU General Public License Version 3 (GPLv3). For more information see the Chips-n-Salsa website.

How to Cite

If you use the Chips-n-Salsa library in your research, please cite the following article which describes the library:

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