Parallel Scatter Search Algorithms for Exam Timetabling

University exam timetabling refers to scheduling exams into predefined days, time periods and rooms, given a set of constraints. Exam timetabling is a computationally intractable optimization problem, which requires heuristic techniques for producing adequate solutions within reasonable execution time. For large numbers of exams and students, sequential algorithms are likely to be time consuming. This paper presents parallel scatter search meta-heuristic algorithms for producing good sub-optimal exam timetables in a reasonable time. Scatter search is a population-based approach that generates solutions over a number of iterations and aims to combine diversification and search intensification. The authors propose parallel scatter search algorithms that are based on distributing the population of candidate solutions over a number of processors in a PC cluster environment. The main components of scatter search are computed in parallel and efficient communication techniques are employed. Empirical results show that the proposed parallel scatter search algorithms yield good speed-up. Also, they show that parallel scatter search algorithms improve solution quality because they explore larger parts of the search space within reasonable time, in contrast with the sequential algorithm.

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Blood Coagulation Algorithm: A Novel Bio-Inspired Meta-Heuristic Algorithm for Global Optimization

Mathematics ◽

10.3390/math9233011 ◽

2021 ◽

Vol 9 (23) ◽

pp. 3011

Author(s):

Drishti Yadav

Keyword(s):

Blood Coagulation ◽

Heuristic Algorithms ◽

Cooperative Behavior ◽

Real Life ◽

Search Space ◽

Population Based ◽

Test Analysis ◽

Search Spaces ◽

Consistent Performance ◽

The Comparative Study

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems.

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Hybrid Harmony Search Algorithms

International Journal of Artificial Intelligence Tools ◽

10.1142/s021821301530001x ◽

2015 ◽

Vol 24 (06) ◽

pp. 1530001 ◽

Cited By ~ 10

Author(s):

Nazmul Siddique ◽

Hojjat Adeli

Keyword(s):

Local Search ◽

Optimization Algorithm ◽

Heuristic Search ◽

Search Algorithm ◽

Harmony Search ◽

Computational Cost ◽

Population Based ◽

Search Algorithms ◽

Harmony Search Algorithm ◽

Solution Quality

Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.

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Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences

Applied Sciences ◽

10.3390/app11178190 ◽

2021 ◽

Vol 11 (17) ◽

pp. 8190

Author(s):

Adnan Ashraf ◽

Sobia Pervaiz ◽

Waqas Haider Bangyal ◽

Kashif Nisar ◽

Ag. Asri Ag. Ibrahim ◽

...

Keyword(s):

Heuristic Algorithms ◽

Random Sequence ◽

Search Space ◽

Population Based ◽

Test Problems ◽

Low Discrepancy Sequences ◽

Experimental Findings ◽

Optimum Solution ◽

The Impact

To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail.

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Parallel scatter search algorithms for exam timetabling. (c2009)

10.26756/th.2009.53 ◽

2009 ◽

Author(s):

Ghia Abdullah Sleiman Haidar

Keyword(s):

Scatter Search ◽

Search Algorithms ◽

Exam Timetabling

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Comparison of Meta-heuristic Algorithms on Benchmark Functions

Academic Perspective Procedia ◽

10.33793/acperpro.02.03.41 ◽

2019 ◽

Vol 2 (3) ◽

pp. 508-517

Author(s):

FerdaNur Arıcı ◽

Ersin Kaya

Keyword(s):

Heuristic Algorithms ◽

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Differential Evolution Algorithm ◽

Population Based ◽

Time Interval ◽

Bee Colony ◽

Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v7i0.115 ◽

2016 ◽

Vol 7 ◽

pp. 1

Author(s):

Ravichander Janapati ◽

Ch. Balaswamy ◽

K. Soundararajan

Keyword(s):

Particle Swarm Optimization ◽

Search Algorithm ◽

Particle Swarm ◽

Search Space ◽

Research Area ◽

Population Based ◽

Wireless Sensor ◽

Cooperative Localization ◽

Localization Algorithm ◽

Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.

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Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210133 ◽

2021 ◽

pp. 1-16

Author(s):

Qianjin Wei ◽

Chengxian Wang ◽

Yimin Wen

Keyword(s):

Rough Set Theory ◽

Attribute Reduction ◽

Search Space ◽

Initial State ◽

Solution Quality ◽

Social Spider ◽

Opposition Based Learning ◽

Social Spider Optimization ◽

Intelligent Optimization Algorithm ◽

Redundancy Detection

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.

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Secure the IoT Networks as Epidemic Containment Game

Symmetry ◽

10.3390/sym13020156 ◽

2021 ◽

Vol 13 (2) ◽

pp. 156

Author(s):

Juntao Zhu ◽

Hong Ding ◽

Yuchen Tao ◽

Zhen Wang ◽

Lanping Yu

Keyword(s):

Heuristic Algorithms ◽

Heuristic Method ◽

Exact Algorithms ◽

Epidemic Threshold ◽

Solution Quality ◽

Sis Model ◽

Linear Programming Formulation ◽

Iot Devices ◽

General Graphs ◽

The Internet Of Things

The spread of a computer virus among the Internet of Things (IoT) devices can be modeled as an Epidemic Containment (EC) game, where each owner decides the strategy, e.g., installing anti-virus software, to maximize his utility against the susceptible-infected-susceptible (SIS) model of the epidemics on graphs. The EC game’s canonical solution concepts are the Minimum/Maximum Nash Equilibria (MinNE/MaxNE). However, computing the exact MinNE/MaxNE is NP-hard, and only several heuristic algorithms are proposed to approximate the MinNE/MaxNE. To calculate the exact MinNE/MaxNE, we provide a thorough analysis of some special graphs and propose scalable and exact algorithms for general graphs. Especially, our contributions are four-fold. First, we analytically give the MinNE/MaxNE for EC on special graphs based on spectral radius. Second, we provide an integer linear programming formulation (ILP) to determine MinNE/MaxNE for the general graphs with the small epidemic threshold. Third, we propose a branch-and-bound (BnB) framework to compute the exact MinNE/MaxNE in the general graphs with several heuristic methods to branch the variables. Fourth, we adopt NetShiled (NetS) method to approximate the MinNE to improve the scalability. Extensive experiments demonstrate that our BnB algorithm can outperform the naive enumeration method in scalability, and the NetS can improve the scalability significantly and outperform the previous heuristic method in solution quality.

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Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

World Journal of Engineering ◽

10.1108/wje-06-2017-0145 ◽

2018 ◽

Vol 15 (2) ◽

pp. 254-272 ◽

Cited By ~ 2

Author(s):

Umamaheswari Elango ◽

Ganesan Sivarajan ◽

Abirami Manoharan ◽

Subramanian Srikrishna

Keyword(s):

Analysis Of Variance ◽

Heuristic Algorithm ◽

Heuristic Algorithms ◽

Statistical Tests ◽

Population Based ◽

Maintenance Scheduling ◽

Content Type ◽

Ant Lion Optimizer ◽

Ant Lion ◽

Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

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