JPEG based steganography methods using Cohort Intelligence with Cognitive Computing and modified Multi Random Start Local Search optimization algorithms

In the last decade, many works compared nonhyperbolic multiparametric travel-time approximations to perform velocity analysis. In these works, some analyses were accomplished, such as accuracy analysis and objective function analysis. However, no previous works compared the optimization algorithms to perform the inversion procedure concerning the processing time and the accuracy of each algorithm. As the shifted hyperbola showed the best results among the unimodal approximations in previous works, it was selected to be used in a comparison with five local search optimization algorithms. Each algorithm was compared concerning the accuracy by the minimization of the calculated curve to the observed curve. The travel-time curves tested here are conventional (PP) and converted wave (PS) reflection events from an offshore model. With this set of tests, it is possible to define which optimization algorithm presents the most reliable result when used with the shifted hyperbola equation concerning the processing time and the accuracy.

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Cohort Intelligence with Cognitive Computing (CICC) and Modified-Multi Random Start Local Search (M-MRSLS) Optimization Algorithms for JPEG Image Steganography (Approach 1 and Approach 2)

Intelligent Systems Reference Library - Optimization Models in Steganography Using Metaheuristics ◽

10.1007/978-3-030-42044-4_4 ◽

2020 ◽

pp. 63-89

Author(s):

Dipti Kapoor Sarmah ◽

Anand J. Kulkarni ◽

Ajith Abraham

Keyword(s):

Local Search ◽

Optimization Algorithms ◽

Image Steganography ◽

Cognitive Computing ◽

Jpeg Image

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Generic Support for Precomputation-Based Global Routing Constraints in Local Search Optimization

Heuristics for Optimization and Learning - Studies in Computational Intelligence ◽

10.1007/978-3-030-58930-1_10 ◽

2020 ◽

pp. 151-165

Author(s):

Renaud De Landtsheer ◽

Fabian Germeau ◽

Thomas Fayolle ◽

Gustavo Ospina ◽

Christophe Ponsard

Keyword(s):

Local Search ◽

Global Routing ◽

Search Optimization

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A Fuzzy Adaptive Simplex Search Optimization Algorithm

Volume 1: 25th Design Automation Conference ◽

10.1115/detc99/dac-8586 ◽

1999 ◽

Author(s):

Mohamed B. Trabia ◽

Xiao Bin Lu

Keyword(s):

Fuzzy Logic ◽

Optimization Algorithms ◽

Test Problems ◽

Objective Functions ◽

Fuzzy Logic Controllers ◽

Design Problems ◽

Simplex Search ◽

Search Optimization ◽

Engineering Design Problems ◽

Fuzzy Adaptive

Abstract Optimization algorithms usually use fixed parameters that are empirically chosen to reach the minimum for various objective functions. This paper shows how to incorporate fuzzy logic in optimization algorithms to make the search adaptive to various objective functions. This idea is applied to produce a new algorithm for minimization of a function of n variables using an adaptive form of the simplex method. The search starts by generating a simplex with n+1 vertices. The algorithm replaces the point with the highest function value by a new point. This process comprises reflecting the point with the highest function value in addition to expanding or contracting the simplex using fuzzy logic controllers whose inputs incorporate the relative weights of the function values at the simplex points. The efficiency of the algorithm is studied using a set of standard minimization test problems. This algorithm generally results in a faster convergence toward the minimum. The algorithm is also applied successfully to two engineering design problems.

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Local search for parallel optimization algorithms for high diminsional optimization problems

MATEC Web of Conferences ◽

10.1051/matecconf/201821004052 ◽

2018 ◽

Vol 210 ◽

pp. 04052 ◽

Cited By ~ 1

Author(s):

Nadia Abd-Alsabour

Keyword(s):

Local Search ◽

Optimization Problems ◽

Optimization Algorithms ◽

Search Algorithms ◽

Parallel Optimization ◽

High Dimensional ◽

Search Process ◽

Local Search Algorithms

Local search algorithms perform an important role when being employed with optimization algorithms tackling numerous optimization problems since they lead to getting better solutions. However, this is not practical in many applications as they do not contribute to the search process. This was not much studied previously for traditional optimization algorithms or for parallel optimization algorithms. This paper investigates this issue for parallel optimization algorithms when tackling high dimensional subset problems. The acquired results show impressive recommendations.

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