scholarly journals Firefly Algorithm based on Euclidean Metric and Dimensional Mutation

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Time Complexity ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Computational Time ◽  
Local Optimum ◽  
Euclidean Metric ◽  
Oscillation Phenomenon ◽  
Attraction Model ◽  
Better Than

Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the "oscillation" phenomenon caused by too many attractions, which makes the convergence speed is too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, In this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.

scholarly journals Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

2020 ◽  
Vol 10 (24) ◽  
pp. 8961
Author(s):  
Peng-Yeng Yin ◽  
Po-Yen Chen ◽  
Ying-Chieh Wei ◽  
Rong-Fuh Day
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
State Of The Art ◽  
Pattern Search ◽  
Experimental Result ◽  
Adaptive Memory ◽  
Glowworm Swarm Optimization ◽  
Metaheuristic Methods ◽  
Swarming Behavior ◽  
Better Than

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.

Harmony Search Algorithm with Opposition-Based Learning for Power System Economic Load Dispatch

2014 ◽  
Vol 1065-1069 ◽  
pp. 3434-3437
Author(s):  
Yi Ning Zhang
Keyword(s):  
Power System ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Opposition Based Learning ◽  
Learning Techniques ◽  
Performance Of Algorithm ◽  
Better Than

A harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system economic load dispatch has been presented. The proposed algorithm integrates the opposition-based learning operation with the improvisation process to prevent the HS-OBL algorithm from being trapped into the local optimum effectively. Besides, a new adjusting strategy is designed to dynamic adjust pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR), which is to further improve the performance of algorithm. The HS-OBL is employed to solve 6 units and 13 units power system, the numerical results indicate that the HS-OBL has perform much better than harmony search(HS) algorithm and other improved algorithms that reported in recent literature.

scholarly journals The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 132 ◽  
Author(s):  
Moath Sababha ◽  
Mohamed Zohdy ◽  
Maged Kafafy
Keyword(s):  
Firefly Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Adaptation Strategy ◽  
Parameter Selection ◽  
Gray Relational Analysis ◽  
Control Parameters ◽  
Exploration And Exploitation ◽  
Absorption Coefficients ◽  
Relational Analysis

As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.

scholarly journals An Improved Crow Search Algorithm for Data Clustering

2020 ◽  
Vol 8 (1) ◽  
pp. 86-101 ◽  
Author(s):  
Vivi Nur Wijayaningrum ◽  
Novi Nur Putriwijaya
Keyword(s):  
Data Clustering ◽  
Search Algorithm ◽  
Search Space ◽  
Large Datasets ◽  
The Other ◽  
Computational Time ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
High Dimensions ◽  
Swarm Optimization

Metaheuristic algorithms are often trapped in local optimum solutions when searching for solutions. This problem often occurs in optimization cases involving high dimensions such as data clustering. Imbalance of the exploration and exploitation process is the cause of this condition because search agents are not able to reach the best solution in the search space. In this study, the problem is overcome by modifying the solution update mechanism so that a search agent not only follows another randomly chosen search agent, but also has the opportunity to follow the best search agent. In addition, the balance of exploration and exploitation is also enhanced by the mechanism of updating the awareness probability of each search agent in accordance with their respective abilities in searching for solutions. The improve mechanism makes the proposed algorithm obtain pretty good solutions with smaller computational time compared to Genetic Algorithm and Particle Swarm Optimization. In large datasets, it is proven that the proposed algorithm is able to provide the best solution among the other algorithms.

scholarly journals Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Muhammad Zakyizzuddin Bin Rosselan ◽  
Shahril Irwan Bin Sulaiman ◽  
Norhalida Othman
Keyword(s):  
Evolutionary Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Evolutionary Programming ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Fitness Value ◽  
Single Objective

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.

A Geometry-Based Accelerated Fusion Clustering Algorithm and its Application in Marine Engineering

2010 ◽  
Vol 108-111 ◽  
pp. 106-111 ◽  
Author(s):  
Tian Zhen Wang ◽  
Yang Liu ◽  
Tian Hao Tang
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Marine Engineering ◽  
Selection Of ◽  
Better Than

In order to solve the problem in k-means algorithm that inappropriate selection of initial clustering centers often causes clustering in local optimum and the time complexity is too high when handling large amounts of data, a fusion clustering algorithm based on geometry is proposed in this paper. The result of experiments shows this algorithm is better than the traditional k-means and the k-means++ algorithms, with higher quality and faster speed. And at last in this paper, we apply it in marine engineering.

NuDist: An Efficient Local Search Algorithm for (Weighted) Partial MaxSAT

2019 ◽  
Vol 63 (9) ◽  
pp. 1321-1337
Author(s):  
Zhendong Lei ◽  
Shaowei Cai
Keyword(s):  
Local Search ◽  
State Of The Art ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Significant Progress ◽  
Weighted Version ◽  
Maximum Satisfiability ◽  
Real World Applications ◽  
Better Than ◽  
Made In

Abstract Maximum satisfiability (MaxSAT) is the optimization version of the satisfiability (SAT). Partial MaxSAT (PMS) generalizes SAT and MaxSAT by introducing hard and soft clauses, while Weighted PMS (WPMS) is the weighted version of PMS where each soft clause has a weight. These two problems have many important real-world applications. Local search is a popular method for solving (W)PMS. Recently, significant progress has been made in this direction by tailoring local search for (W)PMS, and a representative algorithm is the Dist algorithm. In this paper, we propose two ideas to improve Dist, including a clause-weighting scheme and a variable-selection heuristic. The resulting algorithm is called NuDist. Extensive experiments on PMS and WPMS benchmarks from the MaxSAT Evaluations (MSE) 2016 and 2017 show that NuDist significantly outperforms state-of-the-art local search solvers and performs better than state-of-the-art complete solvers including Open-WBO and WPM3 on MSE 2017 benchmarks. Also, empirical analyses confirm the effectiveness of the proposed ideas.

A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method

2020 ◽  
Vol 19 (02) ◽  
pp. 2050015 ◽  
Author(s):  
Forough Shahabi ◽  
Fereshteh Poorahangaryan ◽  
S. A. Edalatpanah ◽  
Homayoun Beheshti
Keyword(s):  
Image Segmentation ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Multilevel Thresholding ◽  
Swarm Optimization ◽  
Variance Minimization ◽  
Image Thresholding ◽  
Otsu Method

Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. Crow Search Algorithm (CSA) is a novel, and efficient swarm-based metaheuristic algorithm that inspired from the way crows storing and retrieving food. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/improved results both in time and uniformity.

scholarly journals Deterministic polynomial factoring and association schemes

2014 ◽  
Vol 17 (1) ◽  
pp. 123-140 ◽  
Author(s):  
Manuel Arora ◽  
Gábor Ivanyos ◽  
Marek Karpinski ◽  
Nitin Saxena
Keyword(s):  
Finite Field ◽  
Time Complexity ◽  
Randomized Algorithms ◽  
Structure Theorem ◽  
State Of The Art ◽  
Association Schemes ◽  
Prime Degree ◽  
Open Question ◽  
Factoring Algorithm ◽  
Better Than

AbstractThe problem of finding a nontrivial factor of a polynomial$f(x)$over a finite field${\mathbb{F}}_q$has many known efficient, but randomized, algorithms. The deterministic complexity of this problem is a famous open question even assuming the generalized Riemann hypothesis (GRH). In this work we improve the state of the art by focusing on prime degree polynomials; let$n$be the degree. If$(n-1)$has a ‘large’$r$-smooth divisor$s$, then we find a nontrivial factor of$f(x)$in deterministic$\mbox{poly}(n^r,\log q)$time, assuming GRH and that$s=\Omega (\sqrt{n/2^r})$. Thus, for$r=O(1)$our algorithm is polynomial time. Further, for$r=\Omega (\log \log n)$there are infinitely many prime degrees$n$for which our algorithm is applicable and better than the best known, assuming GRH. Our methods build on the algebraic-combinatorial framework of$m$-schemes initiated by Ivanyos, Karpinski and Saxena (ISSAC 2009). We show that the$m$-scheme on$n$points, implicitly appearing in our factoring algorithm, has an exceptional structure, leading us to the improved time complexity. Our structure theorem proves the existence of small intersection numbers in any association scheme that has many relations, and roughly equal valencies and indistinguishing numbers.

Vibration-Based Outlier Detection on High Dimensional Data

2016 ◽  
Vol 25 (03) ◽  
pp. 1650013
Author(s):  
Shuyin Xia ◽  
Guoyin Wang ◽  
Hong Yu ◽  
Qun Liu ◽  
Jin Wang
Keyword(s):  
Outlier Detection ◽  
Time Complexity ◽  
State Of The Art ◽  
High Dimensional Data ◽  
Difficult Problem ◽  
Index Structure ◽  
High Dimensional ◽  
Basic Model ◽  
Traditional Approaches ◽  
Better Than

Outlier detection is a difficult problem due to its time complexity being quadratic or cube in most cases, which makes it necessary to develop corresponding acceleration algorithms. Since the index structure (c.f. R tree) is used in the main acceleration algorithms, those approaches deteriorate when the dimensionality increases. In this paper, an approach named VBOD (vibration-based outlier detection) is proposed, in which the main variants assess the vibration. Since the basic model and approximation algorithm FASTVBOD do not need to compute the index structure, their performances are less sensitive to increasing dimensions than traditional approaches. The basic model of this approach has only quadratic time complexity. Furthermore, accelerated algorithms decrease time complexity to [Formula: see text]. The fact that this approach does not rely on any parameter selection is another advantage. FASTVBOD was compared with other state-of-the-art algorithms, and it performed much better than other methods especially on high dimensional data.

Sign in / Sign up

Export Citation Format

Share Document