Discrete optimization problems are often of practical real-world importance as well as computationally intractable. For example, the traveling salesperson, bin packing, and longest common subsequence problems are NP-Hard, as is resource constrained scheduling, and many single-machine scheduling problems. Polynomial time algorithms for such problems are unlikely to exist, and the best known algorithms that guarantee optimal solutions have a worst-case exponential runtime. It is thus common to use stochastic local search and other metaheuristics. Stochastic local search algorithms begin at a random search state, and apply a sequence of neighbor transitions to nearby search states. This includes perturbative algorithms like simulated annealing and hill climbers, where each search state is a complete candidate feasible solution, and a mutation operator makes a small random modification to move to another local candidate solution; and also includes constructive algorithms like stochastic samplers, where each search state is a partial solution that is iteratively transformed into a complete solution. Stochastic local search algorithms do not guarantee optimal solutions. However, they often find near-optimal solutions in much less time than systematic search. They also offer an anytime property, where solution quality improves with runtime.

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. Its focus is discrete optimization, but also supports continuous optimization. The library provides a variety of solution representations, including BitVector and IntegerVector classes, indexable vectors of bits and integers, respectively, along with corresponding mutation operators. The library utilizes our significant prior research on permutation optimization problems, providing an extensive set of mutation operators for permutations, including window-limited mutation. It uses the JavaPermutationTools (JPT) library for efficiently representing solutions to such problems. For continuous problems, Chips-n-Salsa provides a RealVector class, Gaussian mutation, Cauchy mutation, and uniform mutation. The library includes optimization problems useful for benchmarking metaheuristic implementations, such as the well-known OneMax problem, BoundMax (generalization of OneMax to integers), the Permutation in a Haystack problem, and polynomial root finding.

The repository (https://github.com/cicirello/Chips-n-Salsa) contains the library source code, and programs with examples of key functionality. API and other documentation is hosted on the web (https://chips-n-salsa.cicirello.org/). The library can be integrated into projects using maven via GitHub Packages.