Direct Search Firefly Algorithm for Solving Global Optimization Problems

2016 ◽  
Vol 10 (3) ◽  
pp. 841-860 ◽  
Author(s):  
Mohamed A. Tawhid ◽  
Ahmed F. Ali
Keyword(s):  
Global Optimization ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Direct Search

scholarly journals Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2705
Author(s):  
Nebojsa Bacanin ◽  
Ruxandra Stoean ◽  
Miodrag Zivkovic ◽  
Aleksandar Petrovic ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Global Optimization ◽  
Deep Learning ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Wide Spectrum ◽  
Statistical Tests ◽  
Dropout Rate ◽  
Original Method ◽  
Benchmark Datasets

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

Hybrid orthogonal CMAES for solving global optimization problems

2013 ◽  
Vol 32 (4) ◽  
pp. 981-985
Author(s):  
Ya-fei HUANG ◽  
Xi-ming LIANG ◽  
Yi-xiong CHEN
Keyword(s):  
Global Optimization ◽  
Optimization Problems

Catechize Global Optimization through Leading Edge Firefly Based Zone Routing Protocol

2020 ◽  
Vol 13 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Neha Sharma ◽  
Sherin Zafar ◽  
Usha Batra
Keyword(s):  
Global Optimization ◽  
Routing Protocol ◽  
Firefly Algorithm ◽  
Leading Edge ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Zone Routing Protocol ◽  
End To End Delay ◽  
End To End

Background: Zone Routing Protocol is evolving as an efficient hybrid routing protocol with an extremely high potentiality owing to the integration of two radically different schemes, proactive and reactive in such a way that a balance between control overhead and latency is achieved. Its performance is impacted by various network conditions such as zone radius, network size, mobility, etc. Objective: The research work described in this paper focuses on improving the performance of zone routing protocol by reducing the amount of reactive traffic which is primarily responsible for degraded network performance in case of large networks. The usage of route aggregation approach helps in reducing the routing overhead and also help achieve performance optimization. Methods: The performance of proposed protocol is assessed under varying node size and mobility. Further applied is the firefly algorithm which aims to achieve global optimization that is quite difficult to achieve due to non-linearity of functions and multimodality of algorithms. For performance evaluation a set of benchmark functions are being adopted like, packet delivery ratio and end-to-end delay to validate the proposed approach. Results: Simulation results depict better performance of leading edge firefly algorithm when compared to zone routing protocol and route aggregation based zone routing protocol. The proposed leading edge FRA-ZRP approach shows major improvement between ZRP and FRA-ZRP in Packet Delivery Ratio. FRA-ZRP outperforms traditional ZRP and RA-ZRP even in terms of End to End Delay by reducing the delay and gaining a substantial QOS improvement. Conclusion: The achievement of proposed approach can be credited to the formation on zone head and attainment of route from the head hence reduced queuing of data packets due to control packets, by adopting FRA-ZRP approach. The routing optimized zone routing protocol using Route aggregation approach and FRA augments the QoS, which is the most crucial parameter for routing performance enhancement of MANET.

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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