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

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.

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.

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 Physics-Inspired Optimization Algorithms: A Survey

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Anupam Biswas ◽  
K. K. Mishra ◽  
Shailesh Tiwari ◽  
A. K. Misra
Keyword(s):  
Comparative Studies ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Natural Phenomenon ◽  
Complex Optimization ◽  
Evolution With Time

Natural phenomenon can be used to solve complex optimization problems with its excellent facts, functions, and phenomenon. In this paper, a survey on physics-based algorithm is done to show how these inspirations led to the solution of well-known optimization problem. The survey is focused on inspirations that are originated from physics, their formulation into solutions, and their evolution with time. Comparative studies of these noble algorithms along with their variety of applications have been done throughout this paper.

scholarly journals Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea

2020 ◽  
Vol 10 (11) ◽  
pp. 3970 ◽  
Author(s):  
Mohammad Nasir ◽  
Ali Sadollah ◽  
Jin Hee Yoon ◽  
Zong Woo Geem
Keyword(s):  
Comparative Study ◽  
Computer Science ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Fields Of Study ◽  
Complex Optimization ◽  
Near Future

Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future.

scholarly journals Artificial God Optimization - A Creation

2020 ◽  
Vol 13 (1) ◽  
pp. 41
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
The World ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence, the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field, which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm, which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

Introducing Genetic Algorithm as an Intelligent Optimization Technique

2014 ◽  
Vol 568-570 ◽  
pp. 793-797 ◽  
Author(s):  
Ali Ashkzari ◽  
Aydin Azizi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Biological Evolution ◽  
Search Method ◽  
Intelligent Optimization ◽  
Problem Solving Strategies ◽  
Complex Optimization ◽  
Survival Of The Fittest

The Genetic Algorithm (GA) is a stochastic global search method that mimics the metaphor of natural biological evolution. GA operates on a population of potential solutions applying the principle of survival of the fittest to produce (hopefully) better and better approximations to a solution. Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies. Here, in this paper genetic algorithm is introduced as an optimization technique.

scholarly journals Artificial God Optimization – A Creation

2020 ◽  
Vol 7 (6) ◽  
pp. 01-10
Author(s):  
Satish Gajawada ◽  
Hassan Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Complex Optimization ◽  
Primary Focus

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

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’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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