maths.ts
Version:
Math utilities library for TypeScript, JavaScript and Node.js
81 lines (80 loc) • 3.51 kB
JavaScript
;
/**
* @author Hector J. Vasquez <ipi.vasquez@gmail.com>
*
* @licence
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
Object.defineProperty(exports, "__esModule", { value: true });
const _debug = require("debug");
const integers_1 = require("../discrete/integers");
const debug = _debug('mh::sa');
/**
* Simulated annealing metaheuristic seeks for a global optimal solution for
* a given problem. This algorithm is inspired by the steel annealing process.
* @param problem The problem which must have a solution generator, a
* calculator for the solution and a neighbor generator for each solution.
* @param nNeighbors The number of neighbors for the solution generated each
* cycle.
* @param kDiffer The percentage change of each neighbor from the solution.
* @param t0 The initial temperature where to start the algorithm. Default 100.
* @param tf The final temperature where to end the algorithm. Default 0.
* @param tDecrease Sets the temperature decrease. Default nextT = T - 0.1.
* @param sRepetitions The maximum number of repetitions for a given
* solution before temperature drops down.
* @returns The solution found by this algorithm.
*/
function simulatedAnnealing(problem, nNeighbors = 1, kDiffer = 2, t0 = 100, tf = 0, tDecrease = decrease, sRepetitions = 5) {
// Generates an initial solution
let solution = problem.generateSolution();
// Calculates the 'energy' of the initial solution
let sEnergy = problem.solutionValue(solution);
let k = 0;
for (let t = t0; t > tf;) {
// Generates nNeighbor candidates solutions from the neighborhood of
// s, then picks one.
const cSolution = problem
.generateNeighbors(solution, kDiffer, nNeighbors)[integers_1.randInt(0, nNeighbors)];
// Calculates 'energy' of candidate solution
const csEnergy = problem.solutionValue(cSolution);
// Calculates the difference between 'energies'
const AE = sEnergy - csEnergy;
// Informative, only when debugging
debug('Temperature = ' + t, 'e^(AE/t) = ' + Math.exp(AE / t));
debug('curS: ' + solution, 'canS: ' + cSolution);
debug('curS value: ' + sEnergy, 'canS value: ' + csEnergy);
if (AE < 0) {
// If candidate solution is a better solution update
solution = cSolution;
sEnergy = csEnergy;
debug('Update, better solution!');
}
else if (Math.random() >= Math.exp(AE / t)) {
// If candidate solution is not a better solution but the
// temperature is high enough then update
solution = cSolution;
sEnergy = csEnergy;
debug('Update, high enough temperature!');
t = tDecrease(t);
}
else if (k++ === sRepetitions) {
k = 0;
t = tDecrease(t);
}
}
return solution;
}
exports.simulatedAnnealing = simulatedAnnealing;
function decrease(t) {
return t - .1;
}