scholarly journals An Improved Electromagnetic Field Optimization for the Global Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Alkin Yurtkuran
Keyword(s):  
Electromagnetic Field ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Golden Ratio ◽  
New Physics ◽  
Population Based ◽  
Statistical Testing ◽  
Optimal Point ◽  
Bee Colony ◽  
Solution Generation

Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.

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.

Image Segmentation Using Electromagnetic Field Optimization (EFO) in E-Commerce Applications

2019 ◽  
Vol 10 (3) ◽  
pp. 76-91
Author(s):  
Pankaj Upadhyay ◽  
Jitender Kumar Chhabra
Keyword(s):  
Electromagnetic Field ◽  
Image Segmentation ◽  
Golden Ratio ◽  
Optimal Solution ◽  
Image Data ◽  
Population Based ◽  
Vital Role ◽  
Data Set ◽  
Automatic Clustering ◽  
Segmentation Of Images

Image recognition plays a vital role in image-based product searches and false logo identification on e-commerce sites. For the efficient recognition of images, image segmentation is a very important and is an essential phase. This article presents a physics-inspired electromagnetic field optimization (EFO)-based image segmentation method which works using an automatic clustering concept. The proposed approach is a physics-inspired population-based metaheuristic that exploits the behavior of electromagnets and results into a faster convergence and a more accurate segmentation of images. EFO maintains a balance of exploration and exploitation using the nature-inspired golden ratio between attraction and repulsion forces and converges fast towards a globally optimal solution. Fixed length real encoding schemes are used to represent particles in the population. The performance of the proposed method is compared with recent state of the art metaheuristic algorithms for image segmentation. The proposed method is applied to the BSDS 500 image data set. The experimental results indicate better performance in terms of accuracy and convergence speed over the compared algorithms.

scholarly journals An efficient of estimation stages for segmentation skin lesions based optimization algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 402
Author(s):  
Nooraldeen Raaoof Hadi ◽  
H. K. Latif ◽  
Mohanad Aljanabi
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Filter Design ◽  
Design Method ◽  
Median Filter ◽  
Skin Lesions ◽  
Population Based ◽  
Bee Colony ◽  
Design Systems ◽  
Filter Factors

Modern dermatology distinguishes premature diagnosis for example an important part in reducing the death percentage and promising less aggressive treatment for patients. The classifications comprise various stages that must be selected suitably using the characteristics of the filter pointing to get a dependable analysis. The dermoscopic images hold challenges to be faced and overcome to enhance the automatic diagnosis of hazardous lesions. It is calculated to survey a different metaheuristic and evolutionary computing working for filter design systems. Approximately general computing techniques are observed to improve features of infect design method. Nevertheless, the median filter (MF) is normally multimodal with respect to the filter factors and so, reliable approaches that can provide optimal solutions are required. The design of MF depends on modern artificial swarm intelligence technique (MASIT) optimization algorithm which has proven to be more effective than other population-based algorithms to improve of estimation stages for segmentation skin lesions. A controlled artificial bee colony (ABC) algorithm is advanced for solving factors optimization problems and, also the physical-programming-depend on ABC way is applied to proposal median filter, and the outcomes are compared to another approaches.

scholarly journals Application of Firefly Optimization Technique for Solving Convex Economic Load Dispatch of Generation On Nigerian 330 kV, 24-Bus Grid System

2018 ◽  
Vol 3 (5) ◽  
pp. 77 ◽  
Author(s):  
Ganiyu Adedayo Ajenikoko ◽  
O. E. Olabode ◽  
A. E. Lawal
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Cost Model ◽  
Optimization Technique ◽  
Inequality Constraints ◽  
Population Based ◽  
Economic Load Dispatch ◽  
Fuel Cost ◽  
Firefly Optimization ◽  
Bus System

Firefly optimization is a population based technique in which the attractiveness of a firefly is determined by its attractiveness which is then encoded as the objective function of the optimization problems. Firefly algorithm is one of the newest meta-heuristic algorithms based on the mating or flashing behavior of fireflies. Economic load dispatch of generation allocates power generation to match load demand at minimal possible cost without violating power units and system constraints. This paper presents application of firefly optimization technique (FFOT) for solving convex economic load dispatch of generation. The economic load dispatch problem was formulated to minimize the total fuel cost for the heat optimal combination of thermal generators without violating any of the system constraints using quadratic fuel cost model of Sapele, Delta, Afam and Egbin power stations as case studies. The equality and inequality constraints used on the system were the power balance equation and the transmission line constraints respectively. Firefly optimization technique was then developed using appropriate control parameters for a faster convergence of the technique. The optimization technique was tested and validated on the IEEE 30-bus system and Nigerian 24-bus system. The results obtained from the IEEE 30-bus system were compared to published results obtained via Differential Evolution (DE), Ant Colony Optimization (ACO) and Genetic Algorithm (GA). The comparison confirms the superiority, fast convergence and proficiency of the algorithm.

Harmony Search Algorithm and its Variants

2015 ◽  
Vol 29 (08) ◽  
pp. 1539001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
The Past

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

A Bees Life Algorithm for Cloud Computing Services Selection

2012 ◽  
pp. 31-46
Author(s):  
Salim Bitam ◽  
Mohamed Batouche ◽  
El-Ghazali Talbi
Keyword(s):  
Cloud Computing ◽  
Optimization Problems ◽  
Population Based ◽  
Computational Time ◽  
Future Research ◽  
Bee Colony ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Future Research Directions ◽  
Population Based Algorithm

In recent years, the scientific community has begun to model and solve complex optimization problems using bio-inspired methods. Such problems cannot be solved exactly by traditional methods within a reasonable complexity in terms of computer capacities or computational times. However, bio-inspired methods provide near optimal solutions in realist conditions such as cost, capacity, and computational time. In this chapter, the authors propose a new population-based algorithm called the Bees Life Algorithm (BLA). It is applied to solve the cloud computing services selection with quality of service (QoS) requirements. It is considered as swarm-based algorithm, which closely imitates the life of the bee colony. It follows the two important behaviors in the nature of bees, reproduction and food foraging. Bees life algorithm can be applied to the combinatorial optimization problems as well as to the functional optimization problems. An experimental study has been conducted in order to demonstrate the performance and the efficiency of the proposal and its robustness. After comparisons with genetic algorithm (GA) as referential algorithm in this field, the obtained results showed the BLA performance and effectiveness. Finally, promising future research directions are examined to show the BLA usefulness for research in the cloud computing and computational intelligence areas.

A State-of-the-Art Review of Artificial Bee Colony in the Optimization of Single and Multiple Criteria

2013 ◽  
Vol 4 (4) ◽  
pp. 23-45 ◽  
Author(s):  
B. S. P. Mishra ◽  
S. Dehuri ◽  
G.-N. Wang
Keyword(s):  
Honey Bees ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Exact Algorithm ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Time And Space Complexity

Nowadays computers are used to solve a variety and multitude of complex problems facing in every sphere of peoples’ life. However, many of the problems are intractable in nature exact algorithm might need centuries to manage with formidable challenges. In such cases heuristic or in a broader sense meta-heuristic algorithms that find an approximate solution but have acceptable time and space complexity play indispensable role. In this article, the authors present a state-of-the-art review on meta-heuristic algorithm popularly known as artificial bee colony (ABC) inspired by honey bees. Moreover, the ABC algorithm for solving single and multi-objective optimization problems have been studied. A few potential application areas of ABC are highlighted as an end note of this article.

scholarly journals Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions

2019 ◽  
Vol 22 (2) ◽  
pp. 263-280 ◽  
Author(s):  
F. Soghrati ◽  
R. Moeini
Keyword(s):  
Reservoir Operation ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Gravitational Search Algorithm ◽  
Operation Time ◽  
Optimal Operation ◽  
Large Reservoir ◽  
Operation Optimization ◽  
Bee Colony

Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in order to improve the performance of proposed algorithms to solve large-scale problems, two constrained versions of these algorithms have been proposed, in which in these algorithms the problem constraints have been explicitly satisfied. Comparison of the results shows that using the proposed algorithm leads to better results with low computational costs in comparison with other available methods such as genetic algorithm (GA), standard and improved particle swarm optimization (IPSO) algorithm, honey-bees mating optimization (HBMO) algorithm, ant colony optimization algorithm (ACOA), and gravitational search algorithm (GSA). Therefore, the proposed algorithms are capable algorithms to solve large reservoir operation optimization problems.

scholarly journals A Simple and Efficient Artificial Bee Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yunfeng Xu ◽  
Ping Fan ◽  
Ling Yuan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Population Based ◽  
Search Pattern ◽  
Stochastic Algorithms ◽  
Search Process ◽  
Stochastic Algorithm ◽  
Bee Colony ◽  
Simulation Results

Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

Sign in / Sign up

Export Citation Format

Share Document