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.

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.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

Metaheuristic Techniques of Parameter Estimation of Solar PV Cell

2021 ◽  
pp. 219-243
Author(s):  
Nikita Rawat ◽  
Padmanabh Thakur
Keyword(s):  
Parameter Estimation ◽  
Harmony Search ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Absolute Maximum ◽  
Solar Pv ◽  
Pv Cell ◽  
Nonlinear Relation ◽  
Metaheuristic Techniques ◽  
Solution Accuracy

The performance and efficiency of a solar PV cell are greatly dependent on the precise estimation of its current-voltage (I-V) characteristic. Usually, it is very difficult to estimate accurate I-V characteristics of solar PV due to the nonlinear relation between current and voltage. Metaheuristic optimization techniques, on the other hand, are very powerful tools to obtain solutions to complex non-linear problems. Hence, this chapter presents two metaheuristic algorithms, namely particle swarm optimization (PSO) and harmony search (HS), to estimate the single-diode model parameters. The feasibility of the metaheuristic algorithms is demonstrated for a solar cell and its extension to a photovoltaic solar module, and the results are compared with the numerical method, namely the Newton Raphson method (NRM), in terms of the solution accuracy, consistency, absolute maximum power error, and computation efficiency. The results show that the metaheuristic algorithms were indeed capable of obtaining higher quality solutions efficiently in the parameter estimation problem.

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 Multi-objective optimization of an industrial styrene reactor using Harmony Search Algorithm

2013 ◽  
pp. 190-196
Author(s):  
Saurabh Shukla ◽  
Ankit Anand
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Industrial Practice ◽  
Multi Objective ◽  
Decision Variables ◽  
Optimization Study ◽  
Study Results

Multi-objective optimization of industrial styrene reactor is done using Harmony Search algorithm. Harmony search algorithm is a recently developed meta-heuristic algorithm which is inspired by musical improvisation process aimed towards obtaining the best harmony. Three objective functions – productivity, selectivity and yield are optimized to get best combination of decision variables for styrene reactor. All possible cases of single and multi-objective optimization have been considered. Pareto optimal sets are obtained as a result of the optimization study. Results reveal that optimized solution using harmony search algorithm gives better operating conditions than industrial practice.

scholarly journals Analysis of Stiffness Reduction Coefficient of Conventionally Reinforced Concrete Coupling Beams on the Bias of Strut-and-Tie Model

2020 ◽  
Vol 13 (5) ◽  
pp. 82-89
Author(s):  
Zhangqi Hu ◽  
◽  
Weirong Lv ◽  
Yusheng Wu ◽  
Miao Zhang
Keyword(s):  
Reinforced Concrete ◽  
Reinforcement Ratio ◽  
Longitudinal Reinforcement ◽  
Calculation Methods ◽  
Analysis Model ◽  
Coupling Beams ◽  
Stiffness Reduction ◽  
Depth Ratio ◽  
Study Results ◽  
Reduction Coefficient

Stiffness reduction coefficient of coupling beams (κ) can reflect the stiffness degradation degree at yield and significantly affect the seismic response and the internal force distribution. However, existing calculation methods do not consider the influencing factors comprehensively and have a limited application scope. To effectively predict the stiffness reduction coefficient of conventionally reinforced concrete coupling beams (CCBs), a simplified analysis model was established, and analysis and parameter modification were also implemented. Then, an equation with comprehensive consideration, wide application, and high accuracy was proposed. The proposed equation was verified by comparison with existing test data and calculation methods, and parametric analysis was performed to investigate the independent factors, including the span–depth ratio, longitudinal reinforcement ratio, stirrup ratio and concrete compressive strength. Results show that the independent factors are related to each other, and the span–depth ratio has the greatest influence on the stiffness reduction coefficient of CCBs. Furthermore, κ significantly increases with the longitudinal reinforcement ratio when the coupling beam has a large span–depth ratio, but the stirrup ratio has a bigger role when the span-depth ratio is small. Finally, on the basis of the analysis results, suggestions are made to improve the stiffness reduction coefficient of CCBs. The study results provide a reference for the design and optimization of shear wall and core tube structures.

A Survey of Computational Intelligence Algorithms and Their Applications

2020 ◽  
pp. 1886-1929
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Computational Intelligence ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Evolutionary Strategy ◽  
General Process ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
New Algorithms

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

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