Chips-n-Salsa
· 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. Chips-n-Salsa now also includes genetic
algorithms as well as evolutionary algorithms more generally. The
library very 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, as well as common crossover
operators for use with evolutionary algorithms. 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.
