A Hybrid Nature-Inspired Artificial Bee Colony Algorithm for Uncapacitated Examination Timetabling Problems

2015 ◽  
Vol 24 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Asaju La’aro Bolaji ◽  
Ahamad Tajudin Khader ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah
Keyword(s):  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Second Phase ◽  
Examination Timetabling ◽  
Bee Colony ◽  
Population Based Algorithm

AbstractThis article presents a Hybrid Artificial Bee Colony (HABC) for uncapacitated examination timetabling. The ABC algorithm is a recent metaheuristic population-based algorithm that belongs to the Swarm Intelligence technique. Examination timetabling is a hard combinatorial optimization problem of assigning examinations to timeslots based on the given hard and soft constraints. The proposed hybridization comes in two phases: the first phase hybridized a simple local search technique as a local refinement process within the employed bee operator of the original ABC, while the second phase involves the replacement of the scout bee operator with the random consideration concept of harmony search algorithm. The former is to empower the exploitation capability of ABC, whereas the latter is used to control the diversity of the solution search space. The HABC is evaluated using a benchmark dataset defined by Carter, including 12 problem instances. The results show that the HABC is better than exiting ABC techniques and competes well with other techniques from the literature.

Hybrid Harmony Search Algorithm for Global Optimization Problems

2014 ◽  
Vol 1065-1069 ◽  
pp. 3438-3441
Author(s):  
Guo Jun Li
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Search Method ◽  
Local Optima ◽  
Population Based Algorithm ◽  
Better Than

Harmony search (HS) algorithm is a new population based algorithm, which imitates the phenomenon of musical improvisation process. Its own potential and shortage, one shortage is that it easily trapped into local optima. In this paper, a hybrid harmony search algorithm (HHS) is proposed based on the conception of swarm intelligence. HHS employed a local search method to replace the pitch adjusting operation, and designed an elitist preservation strategy to modify the selection operation. Experiment results demonstrated that the proposed method performs much better than the HS and its improved algorithms (IHS, GHS and NGHS).

Performance Study of Harmony Search Algorithm for Multilevel Thresholding

2016 ◽  
Vol 25 (4) ◽  
pp. 473-513 ◽  
Author(s):  
Salima Ouadfel ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Performance Study ◽  
Bacterial Foraging Optimization ◽  
Bee Colony ◽  
Fitness Value ◽  
Multiple Thresholds

AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.

scholarly journals An Evolutionary Approach to Improve the Halftoning Process

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1636
Author(s):  
Noé Ortega-Sánchez ◽  
Diego Oliva ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Angel A. Juan
Keyword(s):  
Visual Information ◽  
Search Algorithm ◽  
Harmony Search ◽  
Similarity Index ◽  
Structural Similarity ◽  
Search Space ◽  
Adaptive Method ◽  
Harmony Search Algorithm ◽  
Efficient Exploration

The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; a possible benefit of this task is the reduction of ink when graphics are printed. The human eye is not able to detect the absence of information, but the printed image stills have good quality. The most used method for halftoning is called Floyd-Steinberger, and it defines a specific matrix for the halftoning conversion. However, most of the proposed techniques in halftoning use predefined kernels that do not permit adaptation to different images. This article introduces the use of the harmony search algorithm (HSA) for halftoning. The HSA is a popular evolutionary algorithm inspired by the musical improvisation. The different operators of the HSA permit an efficient exploration of the search space. The HSA is applied to find the best configuration of the kernel in halftoning; meanwhile, as an objective function, the use of the structural similarity index (SSIM) is proposed. A set of rules are also introduced to reduce the regular patterns that could be created by non-appropriate kernels. The SSIM is used due to the fact that it is a perception model used as a metric that permits comparing images to interpret the differences between them numerically. The aim of combining the HSA with the SSIM for halftoning is to generate an adaptive method that permits estimating the best kernel for each image based on its intrinsic attributes. The graphical quality of the proposed algorithm has been compared with classical halftoning methodologies. Experimental results and comparisons provide evidence regarding the quality of the images obtained by the proposed optimization-based approach. In this context, classical algorithms have a lower graphical quality in comparison with our proposal. The results have been validated by a statistical analysis based on independent experiments over the set of benchmark images by using the mean and standard deviation.

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

scholarly journals A Reward Population-Based Differential Genetic Harmony Search Algorithm

Algorithms ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 23
Author(s):  
Yang Zhang ◽  
Jiacheng Li ◽  
Lei Li
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Memory Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Global Search ◽  
Calculation Results ◽  
Optimal Average ◽  
Average Fitness ◽  
Harmony Memory

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

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