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.

A deterministic semi-infinite global optimization method to design slotless permanent magnet machines

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zahia Amrouchi ◽  
Frederic Messine ◽  
Clement Nadal ◽  
Mohand Ouanes
Keyword(s):  
Global Optimization ◽  
Permanent Magnet ◽  
Analytical Model ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Angular Position ◽  
Torque Ripple ◽  
Deterministic Global Optimization ◽  
Content Type ◽  
Torque Ripples

Purpose In this work, a method to design a slotless permanent magnet machine (SPMM) based on the joint use of an analytical model and deterministic global optimization algorithms is addressed. The purpose of this study is to propose to include torque ripples as an extra constraint in the optimization phase involving de facto the study of a semi-infinite optimization problem. Design/methodology/approach Based on the use of a well-known analytical model describing the electromagnetic behavior of an SPMM, this analytical model has been supplemented by the calculus of the dynamic torque and its ripples to carry out a more accurate optimized sizing method of such an electromechanical converter. As a consequence, the calculated torque depends on a continuous variable, namely, the rotor angular position, resulting in the definition of a semi-infinite optimization problem. The way to solve this kind of semi-infinite problem by discretizing the rotor angular position by using a deterministic global optimization solver, that is to say COUENNE, via the AMPL modeling language is addressed. Findings In this study, the proposed approach is validated on some numerical tests based on the minimization of the magnet volume. Efficient global optimal solutions with torque ripples about 5% (instead of 30%) can be so obtained. Research limitations/implications The analytical model does not use results from the solution of two-dimensional field equations. A strong assumption is put forward to approximate the distribution of the magnetic flux density in the air gap of the SPMM. Originality/value The problem to design an SPMM can be efficiently formulated as a semi-infinite global optimization problem. This kind of optimization problems are hard to solve because they involve an infinity of constraints (coming from a constraint on the torque ripple). The authors show in this paper that by using analytical models, a discretization method and a deterministic global optimization code COUENNE, this problem is efficiently tackled. Some numerical results show that the deterministic global solution of the design can be reached even if the step of discretization is small.

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.

Multiobjective optimization of compensation networks for wireless power transfer systems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manuele Bertoluzzo ◽  
Paolo Di Barba ◽  
Michele Forzan ◽  
Maria Evelina Mognaschi ◽  
Elisabetta Sieni
Keyword(s):  
Frequency Response ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Wireless Power Transfer ◽  
Power Transfer ◽  
Wireless Power ◽  
Nsga Ii ◽  
Content Type ◽  
Starting Point

Purpose The purpose of this paper is to show how the EStra-Many method works on optimization problems characterized by high-dimensionality of the objective space. Moreover, a comparison with a more classical approach (a constrained bi-objective problem solved by means of NSGA-II) is done. Design/methodology/approach The six reactances of a compensation network (CN) for a wireless power transfer system (WPTS) are synthesized by means of an automated optimal design. In particular, an evolutionary algorithm EStra-Many coupled with a sorting strategy has been applied to an optimization problem with four objective functions (OFs). To assess the obtained results, a classical genetic algorithm NSGA-II has been run on a bi-objective problem, constrained by two functions, and the solutions have been analyzed and compared with the ones obtained by EStra-Many. Findings The proposed EStra-Many method identified a solution (CN synthesis) that enhances the WPTS, considering all the four OFs. In particular, to assess the synthesized CN, the Bode diagram of the frequency response and a circuital simulation were evaluated a posteriori; they showed good performance of the CN, with smooth response and without unwanted oscillations when fed by a square wave signal with offset. The EStra-Many method has been able to find a good solution among all the feasible solutions, showing potentiality also for other fields of research, in fact, a solution nondominated with respect to the starting point has been identified. From the methodological viewpoint, the main finding is a new formulation of the many-objective optimization problem based on the concept of degree of conflict, which gives rise to an implementation free from hierarchical weights. Originality/value The new approach EStra-Many used in this paper showed to properly find an optimal solution, trading-off multiple objectives. The compensation network so synthesized by the proposed method showed good properties in terms of frequency response and robustness. The proposed method, able to deal effectively with four OFs, could be applied to solve problems with a higher number of OFs in a variety of applications because of its generality.

“Please tidy up before leaving”: nudging Airbnb guests toward altruistic behavior

2019 ◽  
Vol 13 (4) ◽  
pp. 524-530 ◽  
Author(s):  
Paul Ranson ◽  
Daniel Guttentag
Keyword(s):  
Social Presence ◽  
Design Methodology ◽  
Field Experiments ◽  
Experimental Results ◽  
Altruistic Behavior ◽  
Content Type ◽  
The Social ◽  
Relationship Model ◽  
Social Market ◽  
Practical Implications

Purpose This study aimed to investigate whether increasing the social presence within an Airbnb lodging environment could nudge guests toward altruistic cleaning behaviors. Design/methodology/approach The study was based around a theoretical framework combining the social-market versus money-market relationship model, nudge theory and social presence theory. A series of three field experiments were conducted, in which social presence was manipulated to test its impact on guest cleaning behaviors prior to departure. Findings The experimental results confirmed the underlying hypothesis that an Airbnb listing’s enhanced social presence can subtly induce guests to help clean their rental units prior to departure. Originality/value This study is the first to examine behavioral nudging in an Airbnb context. It is also one of the first field experiments involving Airbnb. The study findings offer clear theoretical and practical implications.

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.

Stellar Mass Black Hole for Engineering Optimization

2017 ◽  
pp. 62-90
Author(s):  
Premalatha Kandhasamy ◽  
Balamurugan R ◽  
Kannimuthu S
Keyword(s):  
Black Hole ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Stellar Mass ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
The Universe

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black holes which are presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the Cuckoo Search, Particle Swarm Optimization and Artificial Bee Colony systems. The experiment results show that the SBO outperforms the existing methods.

scholarly journals Robust Approximate Optimality Conditions for Uncertain Nonsmooth Optimization with Infinite Number of Constraints

Mathematics ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 12 ◽  
Author(s):  
Xiangkai Sun ◽  
Hongyong Fu ◽  
Jing Zeng
Keyword(s):  
Optimality Conditions ◽  
Optimization Problem ◽  
Constraint Qualification ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Sufficient Optimality Conditions ◽  
Convex Constraint ◽  
Approximate Optimality Conditions ◽  
Necessary And Sufficient

This paper deals with robust quasi approximate optimal solutions for a nonsmooth semi-infinite optimization problems with uncertainty data. By virtue of the epigraphs of the conjugates of the constraint functions, we first introduce a robust type closed convex constraint qualification. Then, by using the robust type closed convex constraint qualification and robust optimization technique, we obtain some necessary and sufficient optimality conditions for robust quasi approximate optimal solution and exact optimal solution of this nonsmooth uncertain semi-infinite optimization problem. Moreover, the obtained results in this paper are applied to a nonsmooth uncertain optimization problem with cone constraints.

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