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Article
Publication date: 31 May 2011

A. Kaveh and S. Talatahari

Meta‐heuristic methods are powerful in obtaining the solution of optimization problems. Hybridizing of the meta‐heuristic algorithms provides a scope to improve the searching…

491

Abstract

Purpose

Meta‐heuristic methods are powerful in obtaining the solution of optimization problems. Hybridizing of the meta‐heuristic algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to provide a new hybrid algorithm by adding positive properties of the particle swarm optimization (PSO) algorithms to the charged system search (CSS) to solve constrained engineering optimization problems.

Design/methodology/approach

The main advantages of the PSO consisting of directing the agents toward the global best (obtained by the swarm) and the local best (obtained by the agent itself) are added to the CSS algorithm to improve its performance. In the present approach, similar to the original CSS, each agent is affected by other agents considering the governing laws of electrical physics. However, the kind of the forces can be repulsive and attractive. In order to handle the constraints, the fly‐to‐boundary method is utilized as an improved feasible‐based method.

Findings

Four variants of hybrid methods are proposed. In these algorithms, the charged memory (CM) is changed to save the local best positions of agents. Utilizing this new CM to determine the direction and amount of movement of agents improve the power of the algorithms. When only this memory is utilized (method I), exploitation ability of the algorithm increases and when only two agents from CM in addition to other agents in the current iteration are used, then the exploration ability increases (method II). In order to have a good balance between exploration and exploitation of the algorithms, methods III and IV are proposed, where some agents of the memory and some other from the current agents are utilized. Method IV in which the numbers of used agents from the CM increase linearly, has a better search ability in addition to a powerful exploitation making this variant superior compared to the others.

Originality/value

In this paper, four hybrid methods are presented and applied to some benchmark engineering optimization problems. The new algorithms are compared to those of the other advanced meta‐heuristic methods to illustrate the effectiveness of the proposed methods.

Details

Engineering Computations, vol. 28 no. 4
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 30 September 2014

Gai-Ge Wang, Amir Hossein Gandomi, Xin-She Yang and Amir Hossein Alavi

Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions…

1018

Abstract

Purpose

Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems.

Design/methodology/approach

The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence.

Findings

A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO.

Originality/value

A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

Details

Engineering Computations, vol. 31 no. 7
Type: Research Article
ISSN: 0264-4401

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Book part
Publication date: 12 July 2021

Kuok King Kuok, Chiu Po Chan and Sobri Harun

Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various…

Abstract

Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various non-linear hydrological processes in recent years. However, the convergence rate of the backpropagation NN is relatively slow, and solutions may trap at local minima. Therefore, a new metaheuristic algorithm named as cuckoo search optimisation was proposed to combine with the NN to model the daily rainfall–runoff relationship at Sungai Bedup Basin, Sarawak, Malaysia. Two-year rainfall–runoff data from 1997 to 1998 had been used for model training, while one-year data in 1999 was used for model validation. Input data used are current rainfall, antecedent rainfall and antecedent runoff, while the targeted output is current runoff. This novel NN model is evaluated with the coefficient of correlation (R) and the Nash–Sutcliffe coefficient (E2). Results show that cuckoo search optimisation neural network (CSONN) is able to yield R and E2 to 0.99 and 0.94, respectively, for model validation with the optimal configuration of number of nests (n) = 20, initial discovery rate of alien eggs (painitial) = 0.6, hidden neuron (HN) = 100, iteration number (IN) = 1,000 and learning rate (LR) = 1 for CSONND4 model. The results revealed that the newly developed CSONN is able to simulate runoff accurately using only precipitation and runoff data.

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Article
Publication date: 5 January 2010

A. Kaveh and S. Talatahari

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of…

1669

Abstract

Purpose

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.

Design/methodology/approach

IACO has the capacity to handle continuous and discrete problems by using sub‐optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search‐space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.

Findings

Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search‐space‐updating rule, and it can exchange discrete‐continuous search domain to each other.

Originality/value

The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.

Details

Engineering Computations, vol. 27 no. 1
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 24 June 2013

Gai-Ge Wang, Amir Hossein Gandomi and Amir Hossein Alavi

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization…

661

Abstract

Purpose

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.

Design/methodology/approach

In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.

Findings

This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.

Originality/value

Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.

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Article
Publication date: 6 February 2020

Sajad Ahmad Rather and P. Shanthi Bala

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded…

557

Abstract

Purpose

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).

Design/methodology/approach

In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.

Findings

The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.

Research limitations/implications

The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.

Originality/value

The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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Article
Publication date: 1 March 2022

Babak Talatahari, Mahdi Azizi, Siamak Talatahari, Mohamad Tolouei and Pooya Sareh

In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the…

210

Abstract

Purpose

In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems.

Design/methodology/approach

A total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems.

Findings

The results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors.

Research limitations/implications

This paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering.

Originality/value

CryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems.

Details

Multidiscipline Modeling in Materials and Structures, vol. 18 no. 1
Type: Research Article
ISSN: 1573-6105

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Article
Publication date: 9 February 2023

Qasim Zaheer, Mir Majaid Manzoor and Muhammad Jawad Ahamad

The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been…

444

Abstract

Purpose

The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been drawn to the expanding trend of optimization since the turn of the century. The rate of research can be used to measure the progress and increase of this optimization procedure. This study is phenomenal to understand the optimization process and different algorithms in addition to their application by keeping in mind the current computational power that has increased the implementation for several engineering applications.

Design/methodology/approach

Two-dimensional analysis has been carried out for the optimization process and its approaches to addressing optimization problems, i.e. computational power has increased the implementation. The first section focuses on a thorough examination of the optimization process, its objectives and the development of processes. Second, techniques of the optimization process have been evaluated, as well as some new ones that have emerged to overcome the above-mentioned problems.

Findings

This paper provided detailed knowledge of optimization, several approaches and their applications in civil engineering, i.e. structural, geotechnical, hydraulic, transportation and many more. This research provided tremendous emerging techniques, where the lack of exploratory studies is to be approached soon.

Originality/value

Optimization processes have been studied for a very long time, in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, different techniques and their prediction modes often require high computational strength, such parameters can be mitigated with the use of different techniques to reduce computational cost and increase accuracy.

Details

Engineering Computations, vol. 40 no. 2
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 20 January 2022

Vahid Goodarzimehr, Fereydoon Omidinasab and Nasser Taghizadieh

This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables…

160

Abstract

Purpose

This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables. The PSOGA is an efficient hybridized algorithm to solve optimization problems.

Design/methodology/approach

These algorithms have shown outstanding performance in solving optimization problems with continuous variables. The PSO conceptually models the social behavior of birds, in which individual birds exchange information about their position, velocity and fitness. The behavior of a flock is influencing the probability of migration to other regions with high fitness. The GAs procedure is based on the mechanism of natural selection. The present study uses mutation, random selection and reproduction to reach the best genetic algorithm by the operators of natural genetics. Thus, only identical chromosomes or particles can be converged.

Findings

In this research, using the idea of hybridization PSO and GA algorithms are hybridized and a new meta-heuristic algorithm is developed to minimize the space trusses with continuous design variables. To showing the efficiency and robustness of the new algorithm, several benchmark problems are solved and compared with other researchers.

Originality/value

The results indicate that the hybrid PSO algorithm improved in both exploration and exploitation. The PSO algorithm can be used to minimize the weight of structural problems under stress and displacement constraints.

Details

World Journal of Engineering, vol. 20 no. 3
Type: Research Article
ISSN: 1708-5284

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Article
Publication date: 18 March 2021

Pandiaraj A., Sundar C. and Pavalarajan S.

Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews…

307

Abstract

Purpose

Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper.

Design/methodology/approach

Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively.

Findings

Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved.

Originality/value

This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

Details

Kybernetes, vol. 51 no. 1
Type: Research Article
ISSN: 0368-492X

Keywords

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