An enhanced adaptive butterfly optimization algorithm rigorously verified on engineering problems and implemented to ISAR image motion compensation

2020 ◽  
Vol 37 (9) ◽  
pp. 3543-3566
Author(s):  
Deniz Ustun
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Motion Compensation ◽  
Translational Motion ◽  
Gear Train ◽  
Image Motion ◽  
Content Type ◽  
Beam Design ◽  
Engineering Design Problems ◽  
Engineering Problems

Purpose This study aims to evolve an enhanced butterfly optimization algorithm (BOA) with respect to convergence and accuracy performance for numerous benchmark functions, rigorous constrained engineering design problems and an inverse synthetic aperture radar (ISAR) image motion compensation. Design/methodology/approach Adaptive BOA (ABOA) is thus developed by incorporating spatial dispersal strategy to the global search and inserting the fittest solution to the local search, and hence its exploration and exploitation abilities are improved. Findings The accuracy and convergence performance of ABOA are well verified via exhaustive comparisons with BOA and its existing variants such as improved BOA (IBOA), modified BOA (MBOA) and BOA with Levy flight (BOAL) in terms of various precise metrics through 15 classical and 12 conference on evolutionary computation (CEC)-2017 benchmark functions. ABOA has outstanding accuracy and stability performance better than BOA, IBOA, MBOA and BOAL for most of the benchmarks. The design optimization performance of ABOA is also evaluated for three constrained engineering problems such as welded beam design, spring design and gear train design and the results are compared with those of BOA, MBOA and BOA with chaos. ABOA, therefore, optimizes engineering designs with the most optimal variables. Furthermore, a validation is performed through translational motion compensation (TMC) of the ISAR image for an aircraft, which includes blurriness. In TMC, the motion parameters such as velocity and acceleration of target are optimally predicted by the optimization algorithms. The TMC results are elaborately compared with BOA, IBOA, MBOA and BOAL between each other in view of images, motion parameter and numerical image measuring metrics. Originality/value The outperforming results reflect the optimization and design successes of ABOA which is enhanced by establishing better global and local search abilities over BOA and its existing variants.

A symbiotic organisms search algorithm-based design optimization of constrained multi-objective engineering design problems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deniz Ustun ◽  
Serdar Carbas ◽  
Abdurrahim Toktas
Keyword(s):  
Engineering Design ◽  
Multiple Objectives ◽  
Optimal Solutions ◽  
Design Problems ◽  
Symbiotic Organisms Search ◽  
Content Type ◽  
Multi Objective ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Symbiotic Organisms

Purpose In line with computational technological advances, obtaining optimal solutions for engineering problems has become attractive research topics in various disciplines and real engineering systems having multiple objectives. Therefore, it is aimed to ensure that the multiple objectives are simultaneously optimized by considering them among the trade-offs. Furthermore, the practical means of solving those problems are principally concentrated on handling various complicated constraints. The purpose of this paper is to suggest an algorithm based on symbiotic organisms search (SOS), which mimics the symbiotic reciprocal influence scheme adopted by organisms to live on and breed within the ecosystem, for constrained multi-objective engineering design problems. Design/methodology/approach Though the general performance of SOS algorithm was previously well demonstrated for ordinary single objective optimization problems, its efficacy on multi-objective real engineering problems will be decisive about the performance. The SOS algorithm is, hence, implemented to obtain the optimal solutions of challengingly constrained multi-objective engineering design problems using the Pareto optimality concept. Findings Four well-known mixed constrained multi-objective engineering design problems and a real-world complex constrained multilayer dielectric filter design problem are tackled to demonstrate the precision and stability of the multi-objective SOS (MOSOS) algorithm. Also, the comparison of the obtained results with some other well-known metaheuristics illustrates the validity and robustness of the proposed algorithm. Originality/value The algorithmic performance of the MOSOS on the challengingly constrained multi-objective multidisciplinary engineering design problems with constraint-handling approach is successfully demonstrated with respect to the obtained outperforming final optimal designs.

scholarly journals An Improved Moth-Flame Optimization Algorithm for Engineering Problems

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1234
Author(s):  
Yu Li ◽  
Xinya Zhu ◽  
Jingsen Liu
Keyword(s):  
Optimization Algorithm ◽  
Local Development ◽  
Global Search ◽  
Population Diversity ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Lévy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s global search capability, and the dimension-by-dimension evaluation mechanism is added, which can effectively improve the quality of the solution and balance the global search and local development capability. To substantiate the efficacy of the enhanced algorithm, the proposed algorithm is then tested on a set of 23 benchmark test functions. It is also used to solve four classical engineering design problems, with great progress. In terms of test functions, the experimental results and analysis show that the proposed method is effective and better than other well-known nature-inspired algorithms in terms of convergence speed and accuracy. Additionally, the results of the solution of the engineering problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces.

Optimal solution of engineering design problems through differential gradient evolution plus algorithm: a hybrid approach

2021 ◽  
Author(s):  
Muhammad Farhan Tabassum ◽  
Ali Akgul ◽  
Sana Akram ◽  
Saadia Hassan ◽  
. Saman ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Optimal Solution ◽  
Gear Train ◽  
Rolling Element Bearing ◽  
Local Optimum ◽  
De Algorithm ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Practical Engineering ◽  
Gradient Evolution

Abstract It is very necessary and applicable to optimize all disciplines. In practical engineering problems the optimization has been a significant component. This article presents the hybrid approach named as differential gradient evolution plus (DGE+) algorithm which is the combination of differential evolution (DE) algorithm and gradient evolution (GE) algorithm. DE was used to diversify and GE was used for intensification with a perfect equilibrium between exploration and exploitation with an improvised distribution of dynamic probability and offers a new shake-off approach to prevent premature convergence to local optimum. To describe the success, the proposed algorithm is compared to modern meta-heuristics. To see the accuracy, robustness, and reliability of DGE+ it has been implemented on eight complex practical engineering problems named as: pressure vessel, belleville spring, tension/compression spring, three-bar truss, welded beam, speed reducer, gear train and rolling element bearing design problem, the results revealed that DGE+ algorithm can deliver highly efficient, competitive and promising results.

scholarly journals Advanced arithmetic optimization algorithm for solving mechanical engineering design problems

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255703
Author(s):  
Jeffrey O. Agushaka ◽  
Absalom E. Ezugwu
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Beta Distribution ◽  
Optimization Problems ◽  
Global Optimum ◽  
Natural Logarithm ◽  
Beam Design ◽  
Efficient Performance ◽  
Engineering Design Problems ◽  
Addition And Subtraction

The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its performance. Several other mathematical operators exist with the same or better distributive properties, which can be exploited to enhance the performance of the newly proposed AOA. In this paper, we propose an improved version of the AOA called nAOA algorithm, which uses the high-density values that the natural logarithm and exponential operators can generate, to enhance the exploratory ability of the AOA. The addition and subtraction operators carry out the exploitation. The candidate solutions are initialized using the beta distribution, and the random variables and adaptations used in the algorithm have beta distribution. We test the performance of the proposed nAOA with 30 benchmark functions (20 classical and 10 composite test functions) and three engineering design benchmarks. The performance of nAOA is compared with the original AOA and nine other state-of-the-art algorithms. The nAOA shows efficient performance for the benchmark functions and was second only to GWO for the welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD).

Reverse guidance butterfly optimization algorithm integrated with information cross-sharing

2021 ◽  
pp. 1-22
Author(s):  
Jun Luo ◽  
Qin Tian ◽  
Meng Xu
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Search Space ◽  
Experimental Results ◽  
Neighborhood Search ◽  
Weight Factor ◽  
Exploration And Exploitation ◽  
Local Optima ◽  
Slow Convergence ◽  
Engineering Problems

Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness.

The whirlpool algorithm based on physical phenomenon for solving optimization problems

2019 ◽  
Vol 36 (2) ◽  
pp. 664-690 ◽  
Author(s):  
Yuanyang Zou
Keyword(s):  
Design Methodology ◽  
Optimization Problems ◽  
Physical Phenomenon ◽  
Mathematical Optimization ◽  
Descent Direction ◽  
Content Type ◽  
Beam Design ◽  
Compression Spring ◽  
Engineering Problems ◽  
Design Pressure

Purpose This paper aims to propose a novel nature-inspired optimization algorithm, called whirlpool algorithm (WA), which imitates the physical phenomenon of whirlpool. Design/methodology/approach The idea of this algorithm stems from the fact that the whirlpool has a descent direction and a vertex. Findings WA is tested with two types of models: 29 typical mathematical optimization models and three engineering problems (tension/compression spring design, welded-beam design, pressure vessel design). Originality/value The results shown that the WA is vying compared to the state-of-art algorithms likewise conservative approaches.

Efficient onboard image motion compensation for orbital inclination and eccentricity of geostationary weather satellites

2020 ◽  
Vol 234 (7) ◽  
pp. 1311-1325
Author(s):  
MP Ramachandran ◽  
MK Agarwal ◽  
DA Daniel
Keyword(s):  
Motion Compensation ◽  
Satellite Images ◽  
Software Tool ◽  
Image Motion ◽  
Wind Vector ◽  
Orbital Inclination ◽  
Scanning Mirror ◽  
The Earth ◽  
Detail Image ◽  
Geographical Locations

Image registration is important in geostationary weather satellites. Achieving consistent registration of the images with respect to the geographical locations on the Earth is here of interest. The consistency in the registration between the images is affected whenever the orbital inclination and eccentricity are not zero. The imaging payload has a two-axis scanning mirror to capture the Earth image. The above orbital effects together with scan mirror pointing direction are the factors that cause the misregistration. This paper presents an onboard algorithm that provides the scan compensation angles due to the above factors and achieves consistent registration. The compensation varies every second, which is the time taken for each scan. Hence it is preferred to have computations onboard than to have ground based bulk uplinks for the scan compensation. The paper presents an algorithm that is useful, say, when (i) the onboard computing capabilities are limited, (ii) the navigation accuracies are coarse and (iii) the image resampling is not preferred on the ground and the payload data are directly used for weather applications. The paper also discusses the tests that were carried on the onboard software in order to validate its performance in achieving the consistent registration before launch. This is done by using another independent software tool which is also described in detail. Image motion algorithm was invoked for a couple of days in INSAT 3DR. The atmospheric wind vector deduced directly from the satellite images is given at the end.

scholarly journals Buyer Inspired Meta-Heuristic Optimization Algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 194-219 ◽  
Author(s):  
Sanjoy Debnath ◽  
Wasim Arif ◽  
Srimanta Baishya
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Statistical Test ◽  
Heuristic Optimization ◽  
Product Choice ◽  
Exploration And Exploitation ◽  
The Social ◽  
Engineering Problems ◽  
The Mean

AbstractNature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

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