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 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. It includes several classes 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 many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library.
Chips-n-Salsa is customizable, making extensive use of Java's 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), as well as support for 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 to run length.
The Chips-n-Salsa source code repository is hosted on GitHub; and is licensed under the GNU General Public License Version 3 (GPLv3). The API documentation of Chips-n-Salsa can be found on this site.
In addition to the source code, the repository contains a folder of example programs that show basic usage of the library. There are also JUnit test cases for all library functionality.
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]
The following is a list of publications that either influenced the design of the Chips-n-Salsa library, or which directly used code from the library. In many cases, the papers in the list used earlier implementations of library contents, but which has since been reimplemented. For example, much of the library's code was originally written specifically for permutation optimization problems (i.e., optimization problems, such as the traveling salesperson, for which we are searching the space of permutations). However, much of this code has been heavily refactored (in some cases rewritten from scratch) to use Java generic types enabling application to many other types of optimization problem, including real-valued function optimization. Additionally, the code in the Chips-n-Salsa library has been optimized for execution speed, such as utilizing more efficient random number generators than was used in some of these prior publications.
- Vincent A. Cicirello. Optimizing the Modified Lam Annealing Schedule. Industrial Networks and Intelligent Systems, 7(25), Article e1, December 2020. [PDF] [BIB] [DOI]
- Vincent A. Cicirello. JavaPermutationTools: A Java Library of Permutation Distance Metrics. Journal of Open Source Software, 3(31), 950, November 2018. [PDF] [BIB] [DOI]
- Vincent A. Cicirello. Impact of Random Number Generation on Parallel Genetic Algorithms. In Proceedings of the Thirty-First International Florida Artificial Intelligence Research Society Conference, pages 2-7. AAAI Press, May 2018. [PDF] [BIB] [PUB]
- Vincent A. Cicirello. Variable Annealing Length and Parallelism in Simulated Annealing. In Proceedings of the Tenth International Symposium on Combinatorial Search (SoCS 2017), pages 2-10. AAAI Press, June 2017. [PDF] [BIB] [PUB] [arXiv]
- Vincent A. Cicirello. The Permutation in a Haystack Problem and the Calculus of Search Landscapes. IEEE Transactions on Evolutionary Computation, 20(3):434-446, June 2016. [PDF] [BIB] [DOI]
- Vincent A. Cicirello. On the Effects of Window-Limits on the Distance Profiles of Permutation Neighborhood Operators. In Proceedings of the 8th International Conference on Bio-inspired Information and Communications Technologies, pages 28-35. EAI, December 2014. [PDF] [BIB] [PUB]
- Vincent A. Cicirello and Robert Cernera. Profiling the Distance Characteristics of Mutation Operators for Permutation-Based Genetic Algorithms. In Proceedings of the Twenty-Sixth International Florida Artificial Intelligence Research Society Conference, pages 46-51. AAAI Press, May 2013. [PDF] [BIB] [PUB]
- Vincent A. Cicirello. On the Design of an Adaptive Simulated Annealing Algorithm. In Proceedings of the International Conference on Principles and Practice of Constraint Programming First Workshop on Autonomous Search. AAAI Press, September 2007. [PDF] [BIB]
- Vincent A. Cicirello and Stephen F. Smith. Enhancing Stochastic Search Performance by Value-Biased Randomization of Heuristics. Journal of Heuristics, 11(1):5-34, January 2005. [PDF] [BIB] [DOI]
- Vincent A. Cicirello. Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance. PhD thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, July 2003. [PDF] [BIB]