scholarly journals A Competitive Volleyball Algorithm to Solve Global Optimization with the Engineering Problems

2021 ◽  
Author(s):  
Shuo Sun ◽  
Liang Ma ◽  
Yong Liu
Keyword(s):  
Harmony Search ◽  
Real Life ◽  
Metaheuristic Algorithms ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Engineering Problems ◽  
Heuristic Technique ◽  
Sine Cosine Algorithm ◽  
Low Dimensional ◽  
Np Problems

Abstract In real life, many engineering problems are nonlineble NP problems, in order to solve some of these problems, we put forward a competitive volleyball algorithm.The algorithm proposed in this paper is a meta-heuristic technique based on swarm optimization. It is inspired by the competition between volleyball teams in a league and the improvement in players’ overall abilities in order to win the Most Valuable Player award. Several specific terms relating to competition, such as pre-match reinforcement, single round robin mechanism, optimal strategy constitute the structure of the algorithm. The sensitivity of several parameters of the algorithm is analyzed and tested for three types of benchmark functions: unimodal, high-dimensional multimodal and low-dimensional multimodal functions. Through the use of these three types of test functions, the performance of this algorithm is compared with nine classical metaheuristic algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Soccer League Competition (SLC), League Championship Algorithm (LCA) and Volleyball Premier League (VPL). CVA has been used to solve three real-world engineering problems. The results show that the performance of the CVA is behaviorally promising and better than the other classical metaheuristic algorithms.

A Hybrid of Sine Cosine and Particle Swarm Optimization (HSPS) for Solving Heterogeneous Fixed Fleet Vehicle Routing Problem

2021 ◽  
Vol 12 (1) ◽  
pp. 41-65
Author(s):  
Sandhya Bansal ◽  
Savita Wadhawan
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Approach ◽  
Real Life ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Sine Cosine Algorithm

Heterogeneous fixed fleet vehicle routing problem is a real-life variant of classical VRP, which is a well-established NP-hard optimization problem. In this paper, a hybrid approach based on sine cosine algorithm and particle swarm optimization, namely HSPS, is proposed to solve heterogeneous vehicle routing problem. This hybridization incorporates the strength of both the algorithms for solving this variant. It works in two stages. In first stage, sine cosine algorithm is used to examine the unexplored solution space, and then in next stage, particle swarm optimization is used to exploit the search space. The proposed algorithm has been tested and compared with other algorithms on several benchmark instances. The numerical and statistical results demonstrate that the proposed hybrid is competitive with other existing hybrid algorithms in solving benchmarks with faster convergence rate.

scholarly journals Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner

2018 ◽  
Vol 11 (1) ◽  
pp. 26 ◽  
Author(s):  
Mahdi Bidar ◽  
Samira Sadaoui ◽  
Malek Mouhoub ◽  
Mohsen Bidar
Keyword(s):  
Firefly Algorithm ◽  
Optimization Problems ◽  
Metaheuristic Algorithms ◽  
Exploration And Exploitation ◽  
Improved Firefly Algorithm ◽  
Engineering Problems ◽  
Exploitation And Exploration ◽  
Low Dimensional ◽  
Fuzzy Parameter

Exploitation and exploration are two main search strategies of every metaheuristic algorithm. However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms. To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.

scholarly journals A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques

2018 ◽  
Vol 7 (4.5) ◽  
pp. 455
Author(s):  
Mr. Gireesha. B ◽  
. .
Keyword(s):  
Heuristic Algorithms ◽  
Literature Survey ◽  
Optimization Techniques ◽  
Behaviour Pattern ◽  
Swarm Optimization ◽  
Fitness Functions ◽  
Bee Colony ◽  
Technological Field ◽  
Modern Engineering ◽  
Engineering Problems

From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed. 

scholarly journals An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

2020 ◽  
Vol 10 (6) ◽  
pp. 1910 ◽  
Author(s):  
Hui Li ◽  
Po-Chou Shih ◽  
Xizhao Zhou ◽  
Chunming Ye ◽  
Li Huang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Continuous Optimization ◽  
Harmony Search Algorithm ◽  
Experimental Results ◽  
Metaheuristic Algorithms ◽  
Engineering Problems ◽  
New Harmony ◽  
Continuous Optimization Problems

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

scholarly journals Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem

2021 ◽  
Author(s):  
Serkan Dereli ◽  
Raşit Köker
Keyword(s):  
Pso Algorithm ◽  
Engineering Problem ◽  
Golf Ball ◽  
New Technique ◽  
Successful Implementation ◽  
Robot Arm ◽  
Bee Colony ◽  
Scientific World ◽  
Heuristic Technique ◽  
A New Technique

AbstractThis study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

Research on intelligent clustering of male upper body

2021 ◽  
pp. 004051752110001
Author(s):  
Pengpeng Cheng ◽  
Xianyi Zeng ◽  
Pascal Bruniaux ◽  
Jianping Wang ◽  
Daoling Chen
Keyword(s):  
Artificial Bee Colony ◽  
Young Men ◽  
Cuckoo Search ◽  
The Body ◽  
Upper Body ◽  
Intelligent Algorithm ◽  
Density Peak ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Length Width

To study the upper body characteristics of young men, the body circumference, length, width, thickness, and angle of young men aged 18–25 and 26–35 years were collected to comprehensively characterize the concave and convex features of the front, back, and side of the human body. The Cuckoo Search-Density Peak intelligent algorithm was used to extract the feature factors of the upper body of men, and to cluster them. To verify the effectiveness of the intelligent algorithm, the clustering results of Cuckoo Search-Density Peak, Density Peak, Particle Swarm Optimization-Density Peak algorithm, Ant Colony Optimization-Density Peak algorithm, Genetic Algorithm-Density Peak algorithm, and Artificial Bee Colony-Density Peak algorithm were evaluated by Silouette and F-measures, respectively. The results show that the Cuckoo Search-Density Peak algorithm has the best clustering results and is superior to other algorithms. There are some differences in somatotype characteristics and somatotype indexes between young men aged 18–25 and 26–35 years.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

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.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

2013 ◽  
Vol 46 (11) ◽  
pp. 1465-1484 ◽  
Author(s):  
Weian Guo ◽  
Wuzhao Li ◽  
Qun Zhang ◽  
Lei Wang ◽  
Qidi Wu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Engineering Problems
Sign in / Sign up

Export Citation Format

Share Document