A Hybrid Algorithm Based on Flower Pollination Algorithm and Electro Search for Global Optimization

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Md Fadil Md Esa ◽  
Noorfa Haszlinna Mustaffa ◽  
Nor Haizan Mohamed Radzi
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
High Performance ◽  
Search Strategy ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Method ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Local Search Strategy

In this paper, we have presented a new hybrid optimization method called hybrid Electro-Search algorithm (Eo) and Flower Pollination Optimization Algorithm (FPA) which introduces Eo to FPA. EO-FPA combines the merits of both Eo and FPA by designing on the local-search strategy from Eo and global-search strategy from FPA. The results of the experiments performed with twenty-two well-known benchmark functions show that the proposed algorithm possesses outstanding performance in statistical merit as compared to the original and variant FPA. It is proven that the EO-FPA algorithm requires better formulation to achieve efficiency and high performance to work out with global optimization problems.

scholarly journals An improved firefly algorithm with dynamic self-adaptive adjustment

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0255951
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Yue Shang ◽  
Jingsen Liu
Keyword(s):  
High Performance ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Flower Pollination Algorithm ◽  
Step Size ◽  
Improved Firefly Algorithm ◽  
Random Step ◽  
Self Adaptive

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.

scholarly journals PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-27 ◽  
Author(s):  
Shuangqing Chen ◽  
Yang Liu ◽  
Lixin Wei ◽  
Bing Guan
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Iteration Process ◽  
Optimization Method ◽  
High Dimensional ◽  
Local Optima ◽  
Fireworks Algorithm

Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems.

A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems

2017 ◽  
Vol 35 (4) ◽  
pp. 588-601 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Laila A. Shawky ◽  
Arun Kumar Sangaiah
Keyword(s):  
Global Optimization ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Experimental Results ◽  
Flower Pollination Algorithm ◽  
Content Type ◽  
Flower Pollination

Purpose The purpose of this paper is to present a comparison between two well-known Lévy-based meta-heuristics called cuckoo search (CS) and flower pollination algorithm (FPA). Design/methodology/approach Both the algorithms (Lévy-based meta-heuristics called CS and Flower Pollination) are tested on selected benchmarks from CEC 2017. In addition, this study discussed all CS and FPA comparisons that were included implicitly in other works. Findings The experimental results show that CS is superior in global convergence to the optimal solution, while FPA outperforms CS in terms of time complexity. Originality/value This paper compares the working flow and significance of FPA and CS which seems to have many similarities in order to help the researchers deeply understand the differences between both algorithms. The experimental results are clearly shown to solve the global optimization problem.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Modified Flower Pollination Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1661
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Safaa Saber ◽  
S. S. Askar ◽  
Mohamed Abouhawwash
Keyword(s):  
Global Optimization ◽  
Nonlinear Equation ◽  
Differential Evolution ◽  
Flower Pollination Algorithm ◽  
Test Cases ◽  
Test Functions ◽  
Flower Pollination ◽  
New Variant ◽  
Experimental Findings ◽  
Nonlinear Equation Systems

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

Optimization Method of the Multistage Axial Compressors Using CFD Simulation

2021 ◽  
pp. 2141006
Author(s):  
Grigorii Popov ◽  
Igor Egorov ◽  
Evgenii Goriachkin ◽  
Oleg Baturin ◽  
Daria Kolmakova ◽  
...  
Keyword(s):  
Cfd Simulation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Pressure Ratio ◽  
Optimization Method ◽  
Cfd Modeling ◽  
Turbine Engines ◽  
Stability Margins ◽  
Axial Compressors ◽  
Cfd Models

The current level of numerical methods of gas dynamics makes it possible to optimize compressors using 3D CFD models. However, the methods and means are not sufficiently developed for their wide application. This paper describes a new method for the optimization of multistage axial compressors based on 3D CFD modeling and summarizes the experience of its application. The developed method is a complex system of interconnected components (an effective mathematical model, a parameterizer, and an optimum search algorithm). The use of the method makes it possible to improve or provide the necessary values of the main gas-dynamic parameters of the compressor by changing the shape of the blades and their relative position. The method was tested in solving optimization problems for multistage axial compressors of gas turbine engines (the number of stages from 3 to 15). As a result, an increase in efficiency, pressure ratio, and stability margins was achieved. The presented work is a summary of a long-years investigation of the research team and aims at creating a complete picture of the obtained results for the reader. A brief description of the results of industrial compresses optimization contained in the paper is given as an illustration of the effectiveness of the developed methods.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

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