An efficient algorithm for function optimization: modified stem cells algorithm

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mahsa Paziresh ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Natural Behavior ◽  
Local Optima ◽  
Bee Colony ◽  
Intelligent Behavior ◽  
Improved Performance

AbstractIn this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

Artificial Bee Colony-Based Optimization of Hybrid Wind and Solar Renewable Energy System

2021 ◽  
pp. 819-842
Author(s):  
Diriba Kajela Geleta ◽  
Mukhdeep Singh Manshahia
Keyword(s):  
Renewable Energy ◽  
Artificial Bee Colony ◽  
Hessian Matrix ◽  
Energy System ◽  
Total Annual Cost ◽  
Local Optima ◽  
Bee Colony ◽  
Renewable Energy System ◽  
High Convergence Rate ◽  
Intelligent Behavior

In this chapter, the artificial bee colony (ABC) algorithm was applied to optimize hybrids of wind and solar renewable energy system. The main objective of this research is to minimize the total annual cost of the system by determining appropriate numbers of wind turbine, solar panel, and batteries, so that the desired load can be economically and reliably satisfied based on the given constraints. ABC is a recently proposed meta heuristic algorithm which is inspired by the intelligent behavior of honey bees such as searching for food source and collection and processing of nectar. Instead of gradient and Hessian matrix information, ABC uses stochastic rules to escape local optima and find the global optimal solutions. The proposed methodology was applied to this hybrid system by the help of MATLAB code and the results were discussed. Additionally, it is shown that ABC can be efficiently solve the optimum sizing real-world problems with high convergence rate and reliability. The result was compared with the results of PSO.

Genetic Algorithm Applications to Optimization Modeling

2011 ◽  
pp. 748-754 ◽  
Author(s):  
Pi-Sheng Deng
Keyword(s):  
Portfolio Management ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Solution Space ◽  
Function Optimization ◽  
Hill Climbing ◽  
Local Optima ◽  
Dynamic Memory ◽  
Promising Area ◽  
Improved Performance

Genetic algorithms (GAs) are stochastic search techniques based on the concepts of natural population genetics for exploring a huge solution space in identifying optimal or near optimal solutions (Davis, 1991)(Holland, 1992)(Reeves & Rowe, 2003), and are more likely able to avoid the local optima problem than traditional gradient based hill-climbing optimization techniques when solving complex problems. In essence, GAs are a type of reinforcement learning technique (Grefenstette, 1993), which are able to improve solutions gradually on the basis of the previous solutions. GAs are characterized by their abilities to combine candidate solutions to exploit efficiently a promising area in the solution space while stochastically exploring new search regions with expected improved performance. Many successful applications of this technique are frequently reported across various kinds of industries and businesses, including function optimization (Ballester & Carter, 2004)(Richter & Paxton, 2005), financial risk and portfolio management (Shin & Han, 1999), market trading (Kean, 1995), machine vision and pattern recognition (Vafaie & De Jong, 1998), document retrieval (Gordon, 1988), network topological design (Pierre & Legault, 1998)(Arabas & Kozdrowski, 2001), job shop scheduling (Özdamar, 1999), and optimization for operating system’s dynamic memory configuration (Del Rosso, 2006), among others. In this research we introduce the concept and components of GAs, and then apply the GA technique to the modeling of the batch selection problem of flexible manufacturing systems (FMSs). The model developed in this paper serves as the basis for the experiment in Deng (2007).

scholarly journals A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies

2018 ◽  
Vol 27 (3) ◽  
pp. 489-506 ◽  
Author(s):  
Thanh Tung Khuat ◽  
My Hanh Le
Keyword(s):  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Accurate Estimation ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Agile Methodologies ◽  
Development Processes

Abstract In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results; however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.

scholarly journals A powerful and efficient evolutionary optimization algorithm based on stem cells algorithm for data clustering

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Data Clustering ◽  
High Speed ◽  
Clustering Algorithms ◽  
Implementation Process ◽  
Pso Algorithm ◽  
High Convergence Rate ◽  
Data Points ◽  
Number Of Classes

AbstractThere are many ways to divide datasets into some clusters. One of most popular data clustering algorithms is K-means algorithm which uses the distance criteria for measuring the data correlation. To do that, we should know in advance the number of classes (K) and choose K data points as an initial set to run the algorithm. However, the choice of initial points is a main problem in this algorithm which may cause the algorithm to converge to a local minimum. Some other data clustering algorithms have been proposed to overcome this problem. The methods are Genetic algorithm (GA), Ant Colony Optimization (ACO), PSO algorithm, and ABC algorithms. In this paper, we employ the Stem Cells Optimization algorithm for data clustering. The algorithm was inspired by behavior of natural stem cells in the human body. We developed a new data clustering based on this new optimization scheme which has the advantages such as high convergence rate and easy implementation process. It also avoids local minimums in an intelligent manner. The experimental results obtained by using the new algorithm on different well-known test datasets compared with those obtained using other mentioned methods demonstrate the better accuracy and high speed of the new algorithm.

scholarly journals An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Shouwen Chen ◽  
Zhuoming Xu ◽  
Yan Tang ◽  
Shun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Improved Particle Swarm Optimization ◽  
Inertia Weight ◽  
Ipso Algorithm

Particle swarm optimization algorithm (PSO) is a global stochastic tool, which has ability to search the global optima. However, PSO algorithm is easily trapped into local optima with low accuracy in convergence. In this paper, in order to overcome the shortcoming of PSO algorithm, an improved particle swarm optimization algorithm (IPSO), based on two forms of exponential inertia weight and two types of centroids, is proposed. By means of comparing the optimization ability of IPSO algorithm with BPSO, EPSO, CPSO, and ACL-PSO algorithms, experimental results show that the proposed IPSO algorithm is more efficient; it also outperforms other four baseline PSO algorithms in accuracy.

Lower Distortion in Trajectory Planning for a Rigid-Flexible Manipulator Based on PSO Algorithm

2013 ◽  
Vol 300-301 ◽  
pp. 458-463
Author(s):  
Hai Bin Yin ◽  
Ni Cong Jiang ◽  
Jin Li Xu ◽  
Feng Yun Huang
Keyword(s):  
Trajectory Planning ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Flexible Manipulator ◽  
Specific Site ◽  
Site Conditions ◽  
Site Condition

This paper presents a novel trajectory planning for a multilink rigid-flexible manipulator. An original trajectory is given according to the site condition and requirement of motion, and desired trajectory is determined by using an optimization. To satisfy the requirement of motion with restrictions, the desired trajectory cannot significantly deviate from the original trajectory. Consequently, the lower distortion in trajectory planning should be considered in optimization algorithm. Two different formulations of trajectory are introduced in two optimization algorithms to compare the distortion degree. Finally, the calculation samples are addressed by using specific site conditions and requirements of motion to verify the validity of proposal.

scholarly journals Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

Computation ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 68
Author(s):  
Zenab Mohamed Elgamal ◽  
Norizan Mohd Yasin ◽  
Aznul Qalid Md Sabri ◽  
Rami Sihwail ◽  
Mohammad Tubishat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Local Search ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Optimization Algorithms ◽  
Search Strategies ◽  
Population Diversity ◽  
High Dimensionality ◽  
Local Optima

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

A Novel Defect Evaluation Method for Concrete Structures in Infrared Based on ANN and PSO Algorithm

2010 ◽  
Vol 439-440 ◽  
pp. 552-557 ◽  
Author(s):  
Ben Liang Liang ◽  
Ye Tian
Keyword(s):  
Optimization Algorithm ◽  
Thermal Imaging ◽  
Evaluation Method ◽  
Concrete Structures ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Defect Depth ◽  
Concrete Material ◽  
Infrared Thermal Imaging ◽  
Structure Defects

Abstract: The defect of concrete structures on material level can be expressed by defect depth and defect range. Infrared thermal imaging technology of concrete material defect detection is actually an inverse problem of heat transfer. On the basis of the current research achievements, infrared thermal imaging methods used in concrete structure defects was deduced to a multi-objective function optimization problem. Considering the traditional optimization algorithm slow convergence speed and local minima faults, this paper introduce particle swarm optimization algorithm (PSO) and the BP neural network to detect concrete material defect depth and range.PSO algorithm was used to optimize neural networks connection weights between layers and the network topology. The simulation test results are in good agreement with the experiment results and verify the validity of this method.

scholarly journals Bee Colony Optimization - part II: The application survey

2015 ◽  
Vol 25 (2) ◽  
pp. 185-219 ◽  
Author(s):  
Dusan Teodorovic ◽  
Milica Selmic ◽  
Tatjana Davidovic
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heuristic Method ◽  
Continuous Optimization ◽  
Natural Behavior ◽  
Combinatorial Optimization Problems ◽  
Bee Colony ◽  
Bee Colony Optimization

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications.

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