Dynamic Search Fireworks Algorithm with Adaptive Parameters

2020 ◽  
Vol 11 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Chibing Gong
Keyword(s):  
Swarm Intelligence ◽  
Adaptive Method ◽  
Amplification Coefficient ◽  
Exploration And Exploitation ◽  
Fireworks Algorithm ◽  
Adaptive Parameters ◽  
Dynamic Search ◽  
Reduction Coefficient ◽  
Selection Operator ◽  
Solution Accuracy

As a comparatively new algorithm of swarm intelligence, the dynamic search fireworks algorithm (dynFWA) imitates the explosion procedure of fireworks. With the goal of achieving global optimization and further boosting performance of dynFWA, adaptive parameters are added in this present study, called dynamic search fireworks algorithm with adaptive parameters (dynFWAAP). In this novel dynFWAAP, a self-adaptive method is used to tune the amplification coefficient Ca and the reduction coefficient Cr for fast convergence. To balance exploration and exploitation, the coefficient of amplitude α and the coefficient of sparks β are also adapted, and a new selection operator is proposed. Evaluated on twelve benchmark functions, it is evident from the experimental results that the dynFWAAP significantly outperformed the three variants of fireworks algorithms (FWA) based on solution accuracy and performed best in other four algorithms of swarm intelligence in terms of time cost and solution accuracy.

scholarly journals Dynamic search fireworks algorithm with chaos

2019 ◽  
Vol 13 ◽  
pp. 174830261988955 ◽  
Author(s):  
Chibing Gong
Keyword(s):  
Chaotic Maps ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Amplification Coefficient ◽  
Fireworks Algorithm ◽  
Standard Particle Swarm Optimization ◽  
Study Results ◽  
Dynamic Search ◽  
Reduction Coefficient ◽  
Solution Accuracy

As a relatively new algorithm for swarm intelligence, fireworks algorithm imitates the explosion process of fireworks. A different amplitude in dynamic search fireworks algorithm is presented for an improvement of enhanced fireworks algorithm. This paper integrates chaos with the dynamic search fireworks algorithm so as to further improve the performance and achieve global optimization. Three different variants of dynamic search fireworks algorithm with chaos are introduced and 10 chaotic maps are used to tune either the amplification coefficient [Formula: see text] or the reduction coefficient [Formula: see text]. Twelve benchmark functions are verified in use of the dynamic search fireworks algorithm with chaos (dynamic search fireworks algorithm). The dynamic search fireworks algorithm significantly outperformed the Fireworks Algorithm, enhanced fireworks algorithm, and dynamic search fireworks algorithm based on solution accuracy. The highest performance was seen when dynamic search fireworks algorithm was used with a Gauss/mouse map to tune Ca. Additionally, the dynamic search fireworks algorithm was compared with the firefly algorithm, harmony search, bat algorithm, and standard particle swarm optimization (SPSO2011). Study results indicated that the dynamic search fireworks algorithm has the highest accuracy solution among the five algorithms.

Parameter identification of concrete dams using swarm intelligence algorithm

2017 ◽  
Vol 34 (7) ◽  
pp. 2358-2378 ◽  
Author(s):  
Siqi Dou ◽  
Junjie Li ◽  
Fei Kang
Keyword(s):  
Parameter Identification ◽  
Swarm Intelligence ◽  
Inverse Analysis ◽  
Arch Dam ◽  
Hydraulic Structures ◽  
Concrete Dams ◽  
Concrete Gravity Dam ◽  
Element Analysis ◽  
Content Type ◽  
Fireworks Algorithm

Purpose Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification. Design/methodology/approach Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam. Findings Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency. Originality/value Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

Population Diversity of Particle Swarm Optimization Algorithm on Solving Single and Multi-Objective Problems

2020 ◽  
pp. 312-344
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Diversity Measures ◽  
Computational Resources ◽  
Swarm Intelligence Algorithm

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: the exploration of new possibilities and the exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained.

scholarly journals Population Diversity Maintenance In Brain Storm Optimization Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 83-97 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin ◽  
Qingyu Zhang ◽  
Ruibin Bai
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Search Results ◽  
Brain Storm Optimization ◽  
Convergence And Divergence ◽  
Definition Of ◽  
The Brain

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

scholarly journals Adaptive Mutation Dynamic Search Fireworks Algorithm

Algorithms ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 48 ◽  
Author(s):  
◽  
◽  
◽  
◽  
Keyword(s):  
Adaptive Mutation ◽  
Fireworks Algorithm ◽  
Dynamic Search

scholarly journals Stochastic Diffusion Search Review

2013 ◽  
Vol 4 (3) ◽  
Author(s):  
Mohammad Majid al-Rifaie ◽  
John Mark Bishop
Keyword(s):  
Medical Imaging ◽  
Swarm Intelligence ◽  
Extended Family ◽  
Mathematical Framework ◽  
Computational Creativity ◽  
Exploration And Exploitation ◽  
Stochastic Diffusion ◽  
Two Phases ◽  
Nature Inspired Algorithms ◽  
Stochastic Diffusion Search

AbstractStochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.

scholarly journals Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

2016 ◽  
Vol 12 (11) ◽  
pp. 4515-4522
Author(s):  
K. Deepa ◽  
C. Vivek ◽  
S.Palanivel Rajan
Keyword(s):  
Genetic Algorithm ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Similarity Function ◽  
Human Intervention ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Threshold Setting ◽  
Swarm Intelligence Algorithm

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

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