scholarly journals A comparative study of social group optimization with a few recent optimization algorithms

2020 ◽  
Author(s):  
Anima Naik ◽  
Suresh Chandra Satapathy
Keyword(s):  
Global Optimization ◽  
Social Group ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Search Optimization ◽  
The Social ◽  
Complex Optimization ◽  
Social Group Optimization ◽  
New Algorithms

Abstract From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

scholarly journals Binary social group optimization algorithm for solving 0-1 knapsack problem

2022 ◽  
Vol 11 (1) ◽  
pp. 55-72 ◽  
Author(s):  
Anima Naik ◽  
Pradeep Kumar Chokkalingam
Keyword(s):  
Knapsack Problem ◽  
Social Group ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
High Dimensional ◽  
The Social ◽  
Social Group Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Computational Design ◽  
Heuristic Optimization ◽  
Future Trends ◽  
Excellent Performance ◽  
Comprehensive Investigation ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization

<p>Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><p><strong><br></strong></p><p><strong> Journal of Computational Design and Engineering, 2020.</strong></p><p><strong>DOI: 10.1093/jcde/qwaa037</strong></p>

scholarly journals A Survey on Meta-heuristic Algorithms for Global Optimization Problems

2020 ◽  
pp. 48-60
Author(s):  
Abdel Nasser H. Zaied ◽  
Mahmoud Ismail ◽  
Salwa El-Sayed ◽  
◽  
◽  
...  
Keyword(s):  
Global Optimization ◽  
Real World ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Traditional Methods ◽  
Complex Problems ◽  
First Choice ◽  
Important Field ◽  
Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

Comparative Assessment Of Light-based Intelligent Search And Optimization Algorithms

2020 ◽  
pp. 51-59
Author(s):  
Bilal Alatas ◽  
Harun Bingol
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Design Problems ◽  
Intelligent Optimization ◽  
Intelligent Search ◽  
New Methods ◽  
Intelligent Optimization Algorithms ◽  
Engineering Design Problems ◽  
Complex Optimization ◽  
First Time

Classical optimization and search algorithms are not effective for nonlinear, complex, dynamic large-scaled problems with incomplete information. Hence, intelligent optimization algorithms, which are inspired by natural phenomena such as physics, biology, chemistry, mathematics, and so on have been proposed as working solutions over time. Many of the intelligent optimization algorithms are based on physics and biology, and they work by modelling or simulating different nature-based processes. Due to philosophy of constantly researching the best and absence of the most effective algorithm for all kinds of problems, new methods or new versions of existing methods are proposed to see if they can cope with very complex optimization problems. Two recently proposed algorithms, namely ray optimization and optics inspired optimization, seem to be inspired by light, and they are entitled as light-based intelligent optimization algorithms in this paper. These newer intelligent search and optimization algorithms are inspired by the law of refraction and reflection of light. Studies of these algorithms are compiled and the performance analysis of light-based i ntelligent optimization algorithms on unconstrained benchmark functions and constrained real engineering design problems is performed under equal conditions for the first time in this article. The results obtained show that ray optimization is superior, and effectively solves many complex problems.

Catastrophe-Based Antibody Clone Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4415-4420
Author(s):  
Yu Zhang ◽  
Li Hua Wu ◽  
Zi Qiang Luo
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Natural World ◽  
Population Diversity ◽  
Selection Algorithm ◽  
Intelligent Optimization Algorithms ◽  
Complex Optimization ◽  
Search Capability ◽  
Better Than

In solving complex optimization problems, intelligent optimization algorithms such as immune algorithm show better advantages than traditional optimization algorithms. Most of these immune algorithms, however, have disadvantages in population diversity and preservation of elitist antibodies genes, which will lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. By introducing the catastrophe factor into the ACAMHC algorithm, we propose a novel catastrophe-based antibody clone algorithm (CACA) to solve the above problems. CACA preserves elitist antibody genes through the vaccine library to improve its local search capability; it improves the antibody population diversity by gene mutation that mimics the catastrophe events to the natural world to enhance its global search capability. To expand the antibody search space, CACA will add some new random immigrant antibodies with a certain ratio. The convergence of CACA is theoretically proved. The experiments of CACA compared with the clone selection algorithm (ACAMHC) on some benchmark functions are carried out. The experimental results indicate that the performance of CACA is better than that of ACAMHC. The CACA algorithm provides new opportunities for solving previously intractable optimization problems.

scholarly journals Self-Stabilizing Global Optimization Algorithms for Large Network Graphs

2005 ◽  
Vol 1 (3-4) ◽  
pp. 329-344 ◽  
Author(s):  
Wayne Goddard ◽  
Stephen T. Hedetniemi ◽  
David P. Jacobs ◽  
Pradip K. Srimani
Keyword(s):  
Global Optimization ◽  
Distributed Algorithms ◽  
Optimization Problems ◽  
Dominating Set ◽  
Optimization Algorithms ◽  
Large Network ◽  
Shared Variable ◽  
Large Networks ◽  
Total Dominating Set ◽  
Graph Optimization

The paradigm of self-stabilization provides a mechanism to design efficient localized distributed algorithms that are proving to be essential for modern day large networks of sensors. We provide self-stabilizing algorithms (in the shared-variable ID-based model) for three graph optimization problems: a minimal total dominating set (where every node must be adjacent to a node in the set) and its generalizations, a maximal k-packing (a set of nodes where every pair of nodes are more than distance k apart), and a maximal strong matching (a collection of totally disjoint edges).

MHC-INSPIRED ANTIBODY CLONE ALGORITHM

2010 ◽  
Vol 07 (02) ◽  
pp. 299-318 ◽  
Author(s):  
YU ZHANG ◽  
XUMING CHEN ◽  
LIHUA WU ◽  
ZIQIANG LUO ◽  
XIAOJIE LIU
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Population Diversity ◽  
Histocompatibility Complex ◽  
Intelligent Optimization Algorithms ◽  
Mhc Polymorphism ◽  
Mhc Haplotype ◽  
Complex Optimization ◽  
Search Capability

For solving complex optimization problems in some engineering applications, intelligent optimization algorithms based on biological mechanisms have better performance than traditional optimization algorithms. Most of these intelligent algorithms, however, have disadvantages in population diversity and preservation of elitist antibody genes, which lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. Drawing inspiration from the features of major histocompatibility complex (MHC) in the biological immune system, we propose a novel MHC-inspired antibody clone algorithm (ACAMHC) for solving the above problems. ACAMHC preserves elitist antibody genes through the MHC strings that emulate the MHC haplotype in order to improve its local search capability; it improves the antibody population diversity by gene mutation that mimick the MHC polymorphism to enhance its global search capability. To expand the antibody search space, ACAMHC will add some new random immigrant antibodies with a certain ratio. The convergence of ACAMHC is theoretically proven. The experiments of ACAMHC compared with the canonical clone selection algorithm (CLONALG) on 20 benchmark functions are carried out. The experimental results indicate that the performance of ACAMHC is better than that of CLONALG. The ACAMHC algorithm provides new opportunities for solving previously intractable optimization problems.

scholarly journals Taxonomy of bio-inspired optimization algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 23 ◽  
Author(s):  
Saman M. Almufti ◽  
Ridwan Boya Marqas ◽  
Vaman Ashqi Saeed
Keyword(s):  
Computer Science ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Biological Evolution ◽  
Living Organism ◽  
Complex Optimization ◽  
Biological Field

Bio-Inspired optimization algorithms are inspired from principles of natural biological evolution and distributed collective of a living organism such as (insects, animal, …. etc.) for obtaining the optimal possible solutions for hard and complex optimization problems. In computer science Bio-Inspired optimization algorithms have been broadly used because of their exhibits extremely diverse, robust, dynamic, complex and fascinating phenomenon as compared to other existing classical techniques.This paper presents an overview study on the taxonomy of bio-inspired optimization algorithms according to the biological field that are inspired from and the areas where these algorithms have been successfully applied

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