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. The library most 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. 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:
- Vincent A. Cicirello. Chips-n-Salsa: A Java Library of Customizable, Hybridizable, Iterative, Parallel, Stochastic, and Self-Adaptive Local Search Algorithms. Journal of Open Source Software, 5(52), 2448, August 2020. [PDF] [BIB] [DOI]
Classes related to random number generation.
This package includes classes and interfaces related to implementing metaheuristic search algorithms in general, rather than specific to a particular metaheuristic.
This package includes multithreaded search implementations, as well as classes and interfaces related to implementing multithreaded metaheuristics.
This package includes classes and interfaces directly related to implementing hill climbers.
This package includes classes and interfaces for defining various operators required by simulated annealing and other metaheuristics, such as mutation operators, along with other related classes and interfaces.
This package includes classes that implement operators that create, mutate, etc, BitVectors.
This package includes classes that implement operators that create, mutate, etc, integer valued representations.
This package includes classes that implement mutation operators for permutations, and other operators required by simulated annealing and other related metaheuristics.
This package includes classes that implement operators that create, mutate, etc, the the inputs to functions with real-valued input parameters (represented with type double), such as is required to solve function optimization problems using simulated annealing or other metaheuristics.
Package of classes and interfaces related to representing computational problems, as well as classes implementing a variety of specific computational problems.
Package of classes and interfaces related to representing and solving scheduling problems, which includes implementations of constructive heuristics for scheduling problems.
This package includes classes related to representing solutions to optimization problems.
This package includes classes and interfaces related to implementing multistart metaheuristics (i.e., metaheuristics that periodically restart, and return the best solution across a number of such restarts).
This package includes classes and interfaces directly related to implementing simulated annealing.
This package includes classes and interfaces directly related to implementing stochastic sampling algorithms.