Memetic Algorithm for Constructing Covering Arrays of Variable Strength Based on Global-Best Harmony Search and Simulated Annealing

2018 ◽  
pp. 18-32
Author(s):  
Jimena Timaná ◽  
Carlos Cobos ◽  
Jose Torres-Jimenez
Keyword(s):  
Simulated Annealing ◽  
Memetic Algorithm ◽  
Harmony Search ◽  
Covering Arrays ◽  
Variable Strength ◽  
Strength Based

scholarly journals Metaheuristic algorithms for building Covering Arrays: A review

2016 ◽  
Vol 25 (43) ◽  
pp. 31-45
Author(s):  
Jimena Adriana Timaná-Peña ◽  
Carlos Alberto Cobos-Lozada ◽  
Jose Torres-Jimenez
Keyword(s):  
Simulated Annealing ◽  
Harmony Search ◽  
Test Cases ◽  
Covering Arrays ◽  
Swarm Algorithms ◽  
Ant Colony Algorithms ◽  
Logical Unit ◽  
Minimum Number ◽  
Np Complete ◽  
Selection Of

Covering Arrays (CA) are mathematical objects used in the functional testing of software components. They enable the testing of all interactions of a given size of input parameters in a procedure, function, or logical unit in general, using the minimum number of test cases. Building CA is a complex task (NP-complete problem) that involves lengthy execution times and high computational loads. The most effective methods for building CAs are algebraic, Greedy, and metaheuristic-based. The latter have reported the best results to date. This paper presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms. It is worth noting that simulated annealing-based algorithms have evolved as the most competitive, and currently form the state of the art.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

scholarly journals Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2133
Author(s):  
Elisa Ficarella ◽  
Luciano Lamberti ◽  
Sadik Ozgur Degertekin
Keyword(s):  
Simulated Annealing ◽  
Search Engines ◽  
Line Search ◽  
Linear Optimization ◽  
Harmony Search ◽  
Transversely Isotropic ◽  
Big Bang ◽  
Optical Methods ◽  
Metaheuristic Search ◽  
Big Crunch

This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

A Hybrid Meta-Heuristic Algorithm for Vehicle Routing Problem with Time Windows

2015 ◽  
Vol 24 (06) ◽  
pp. 1550021 ◽  
Author(s):  
Esam Taha Yassen ◽  
Masri Ayob ◽  
Mohd Zakree Ahmad Nazri ◽  
Nasser R. Sabar
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Time Windows ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Routing Problem

Harmony search algorithm, which simulates the musical improvisation process in seeking agreeable harmony, is a population based meta-heuristics algorithm for solving optimization problems. Although it has been successfully applied on various optimization problems; it suffers the slow convergence problem, which greatly hinders its applicability for getting good quality solution. Therefore, in this work, we propose a hybrid meta-heuristic algorithm that hybridizes a harmony search with simulated annealing for the purpose of improving the performance of harmony search algorithm. Harmony search algorithm is used to explore the search spaces. Whilst, simulated annealing algorithm is used inside the harmony search algorithm to exploit the search space and further improve the solutions that are generated by harmony search algorithm. The performance of the proposed algorithm is tested using the Solomon's Vehicle Routing Problem with Time Windows (VRPTW) benchmark. Numerical results demonstrate that the hybrid approach is better than the harmony search without simulated annealing and the hybrid also proves itself to be more competent (if not better on some instances) when compared to other approaches in the literature.

scholarly journals Generalised Adaptive Harmony Search: A Comparative Analysis of Modern Harmony Search

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jaco Fourie ◽  
Richard Green ◽  
Zong Woo Geem
Keyword(s):  
Simulated Annealing ◽  
Comparative Analysis ◽  
Heuristic Algorithms ◽  
Harmony Search ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Population Based ◽  
Particle Swarm Optimisation ◽  
Optimisation Algorithm ◽  
Multimodal Problems

Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search.

Simulated Annealing for Constructing Mixed Covering Arrays

2012 ◽  
pp. 657-664 ◽  
Author(s):  
Himer Avila-George ◽  
Jose Torres-Jimenez ◽  
Vicente Hernández ◽  
Loreto Gonzalez-Hernandez
Keyword(s):  
Simulated Annealing ◽  
Covering Arrays
Sign in / Sign up

Export Citation Format

Share Document