The goal of the problem is to determine a minimum length of the container that contadoi:10.1007/978-3-319-21133-6_9Mhand HifiLabib YousefRecent Advances in Computational Optimization, Springer
These are then post-optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results ...
Interestingly, deep learning algorithms have been used as surrogate models for solving such regression problems. For instance, the surrogate method, which is trained on sample points, has been used in the evolution algorithm (EA) to reduce the computational cost for functional evaluations (FEs) to ...
Simulated annealing:switch from hill climbing to gradient descent(i.e. minimizing cost), start by shaking hard (hard enough bounce out of local minima), and then gradually reduce the intensity of the shaking(not hard enough to dislodge from the global minimum). The algorithm picks a random mo...
Genetic algorithm is used to derive the satisficing decisions of the problem by finding Pareto optimal solutions. The feasibility of solutions is always maintained during the crossover and mutation operations. The efficient set is constantly updated and finally hill climbing method is employed to ...
Gradient-based: Gradient descent Conjugate gradient Quasi-newton Genetic algorithms Hill climbing Simplex / amoeba / Nelder Mead Tensorflow versions The master branch supports Tensorflow 2 versions of the baseline algorithm A2C/A3C PG Algorithms A2C/A3C ACER ACKTR GAE PPO REINFORCE TRPO VMPOAbout...
A Python implementation of a Parallel Hill Climbing algorithm solving the OneMax optimisation problem. Features comprehensive performance analysis across different problem sizes and population parameters. python optimization data-visualization artificial-intelligence hill-climbing algorithm-analysis optimization-algor...
A step counting hill climbing algorithm applied to university examination timetabling Journal of Scheduling, 19 (2016), pp. 479-492 CrossrefView in ScopusGoogle Scholar Cacchiani, Caprara, Roberti, Toth, 2013 V. Cacchiani, A. Caprara, R. Roberti, P. Toth A new lower bound for curriculum-...
actions guarantee generating an optimal path through the search space with a great probability of achieving a global optimal solution. Hill climbing (HC) reviewed in [65], Tabu Search (TS) [23], Great Deluge Algorithm (GDA) [63], Iterated Local Search (ILS) [66], and Greedy Randomized ...
This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strateg