Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems

Tansui, Daranat; Thammano, Arit

doi:10.1109/access.2020.2984023

Cited by 16 publications

(8 citation statements)

References 45 publications

(51 reference statements)

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“…These are nature-inspired behavior algorithms that can search for an optimal or a set of optimal solutions for a complex optimization problem. For example, sea turtle foraging algorithms (STFA) are proposed in (Tansui & Thammano, 2016 for solving continuous optimization problems. The bond energy algorithm (BEA) was developed and used in the database design area to determine how to group and physically place data on a disk (Mehta et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An Optimal Multiquadric Variable Shape Parameter for Boundary Value Problems Using Particle Swarm Optimization

Bacar,

Rawhoudine

2024

JMR

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The multiquadric radial basis function method has been widely used to solve partial differential equations-based problems regarding its flexibility and meshfree characteristics. The accuracy and stability of this method are derived and based on the use of a free-shape parameter that sensibly controls the comportment of the technique. Significant improvements have already been reported and show that variable shape parameters conduct the method to handle problems with striking results compared to global-based techniques. Nevertheless, choosing a suitable set of shape parameters is still an open topic because of the complexity of the method when the number of collocation points increases. The current work proposes a variant particle swarm optimization based on local displacement with attractors to determine the multi-quadratic function's ``best'' optimal variable shape parameter in solving boundary value problems. Based on an initially random set of variable shape parameters, the proposed algorithm first performs and evaluates the errors between the expected exact solution and the approximate solution thoroughly. In the first stage, the particle swarm algorithm search for an optimal set of shape parameters that minimize the error and the conditioning number of the radial basis system matrix. In the second stage, the obtained optimal set of shape parameters is applied to solve the considered problem. In this way, when the number of collocation points increases, the first stage based on particle swarm optimization stabilizes the strategy. It proposes an ``acceptable'' set of shape parameters for the given problem. The proposed method is applied to a set of well-known boundary value problems in one and two-dimensional spaces and compared to other techniques published in the literature. The results show that the proposed method achieves more accurate solutions than recently proposed in the literature.

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Section: Introductionmentioning

confidence: 99%

An Optimal Multiquadric Variable Shape Parameter for Boundary Value Problems Using Particle Swarm Optimization

Bacar,

Rawhoudine

2024

JMR

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“…Global optimization has been long dominated by nature-inspired algorithms 34 – 42 . Their history started in 1980s with the development of several fundamental methods such as genetic algorithms (GAs) 43 , evolutionary algorithms (EAs) 44 , genetic programming 45 , ant systems 46 , although evolutionary strategies (ES) 47 have been proposed for continuous optimization as early as in late 1960s, and can be considered as belonging to the same category.…”

Section: Introductionmentioning

confidence: 99%

High-efficacy global optimization of antenna structures by means of simplex-based predictors

Koziel,

Pietrenko-Dabrowska

2023

Sci Rep

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Design of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the accumulated cost of multiple antenna evaluations. This problem is especially pronounced in the case of global search, frequently performed using nature-inspired algorithms, known for poor computational efficiency. At the same time, global optimization is often required, either due to multimodality of the design task or the lack of sufficiently good starting point. A workaround is to combine metaheuristics with surrogate modeling methods, yet a construction of reliable metamodels over broad ranges of antenna parameters is challenging. This work introduces a novel procedure for global optimization of antenna structures. Our methodology involves a simplex-based automated search performed at the level of approximated operating and performance figures of the structure at hand. The presented approach capitalizes on weakly-nonlinear dependence between the operating figures and antenna geometry parameters, as well as computationally cheap design updates, only requiring a single EM analysis per iteration. Formal convergence of the algorithm is guaranteed by implementing the automated decision-making procedure for reducing the simplex size upon detecting the lack of objective function improvement. The global optimization stage is succeeded by gradient-based parameter refinement. The proposed procedure has been validated using four microstrip antenna structures. Multiple independent runs and statistical analysis of the results have been carried out in order to corroborate global search capability. Satisfactory outcome obtained for all instances, and low average computational cost of only 120 EM antenna simulations, demonstrate superior efficacy of our algorithm, also in comparison with both local optimizers and nature-inspired procedures.

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“…By building suitable mathematical model according to concrete problems, in order to minimize (sometimes maximize) the fitness function of the concrete problem, many actual engineering problems belong to optimization problems. Examples include structural mechanics, power systems and so on [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…r3 is a random variable. r4 is used to choose different search paths, sine or cosine, according to different random values in Eq (1)…”

mentioning

confidence: 99%

A Modified Sine Cosine Algorithm for Solving Optimization Problems

Wang

2021

IEEE Access

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The sine cosine algorithm (SCA) is a newly emerging optimization algorithm. It is easy for sine cosine algorithm (SCA) to sink into premature of the algorithm and obtain a slower convergence rate when solving the complicated optimization problems, especially highly ill-posed problems. A novel modified sine cosine algorithm (MSCA) is put forward for solving the optimization problems. To our limited knowledge, the linear searching path and empirical parameter have not been applied to the related improved sine cosine algorithm. The proposed MSCA improves the search path of original SCA by introducing linear searching path and empirical parameter, effectively avoiding sinking into the local optimal. In addition, the proposed algorithm changes the definition of convergence factor. Two kinds of tests, including 23 benchmark functions test and actual engineering problem tests are adopted to prove the performance of the MSCA. In addition, the performance of the proposed MSCA is compared with SCA by using benchmark functions on different dimensional (D = 30, 50,100 and 500). As expected, the result of comparisons show that the proposed MSCA can better avoid the local optima than both the SCA and other population-based algorithms. And MSCA can obtain the faster convergence than SCA on different dimensions. INDEX TERMS Sine cosine algorithm, linear searching path, nature-inspired algorithm, optimization.

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Hybrid Nature-Inspired Optimization Algorithm: Hydrozoan and Sea Turtle Foraging Algorithms for Solving Continuous Optimization Problems

Cited by 16 publications

References 45 publications

An Optimal Multiquadric Variable Shape Parameter for Boundary Value Problems Using Particle Swarm Optimization

An Optimal Multiquadric Variable Shape Parameter for Boundary Value Problems Using Particle Swarm Optimization

High-efficacy global optimization of antenna structures by means of simplex-based predictors

A Modified Sine Cosine Algorithm for Solving Optimization Problems

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