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A hyperspectral image is taken by infrared imaging spectrometer consist of a continuous series of hundreds of bands. These bands collect spectral information across the electromagnetic spectrum. The presence of high spectral correlation among the bands have necessitated the use of dimensionality reduction in Hyperspectral image. Thus the bands which posses significant information needs to be selected and remaining noisy, correlated ones needs are rejected. In this paper the band selection task is formulated as a multi-objective optimization problem and two objective functions : Entropy and Pearson correlation coefficient are used in the analysis. The Orthogonal Array Design (OAD) is a mathematical procedure to determine few selected combinations which are effective among the total number of possible combination between vectors. It has been suitably applied in single-objective evolutionary algorithms to enhance their exploration capabilities. In this paper the OAD is hybridized with two nature inspired algorithms : Symbiotic Organisms Search (SOS) and Colliding Bodies Optimization (CBO). Resulting two algorithms termed as OAD-MOSOS and OAD-MOCBO have been applied to solve unconstrained and constrained multi-objective optimization problems. The adaptive penalty function is embodied with OAD-MOSOS and OAD-MOCBO to handle the constrained problem. Simulation results on eight benchmark functions reveal that OAD-MOSOS is accurate whereas OAD-MOCBO is computationally efficient. Both the developed algorithms are employed for band reduction in two hyperspectral images of Pavia University and Pavia Center. Simulation results reveal that in both hyperspectral images the number of retained bands using OAD-MOSOS algorithm is lowest and the clustering accuracy achieved is highest among the comparative algorithms based on MOSOS, MOCBO and NSGA-II.

In digital communication, channel equalization plays an important role in mitigating the effects of inter-symbol interference, non-linearity and noise. In case of wireless channels, it also reduces the co-channel and adjacent channel interference. The channel equalizer is placed at the receiver which inherently perform an inverse modeling operation. In this manuscript, equalizers are proposed based on Functional Link Artificial Neural Network (FLANN) for nonlinear communication channel. Three types of FLANN architecture are explored based on: Trigonometric, Chebyshev and Legendre polynomial-based expansions. The weight of these FLANN architectures are trained by a quantum Aquila optimization algorithm (QAOA). In this manuscript the quantum entanglement principle is embodied to improve the performance of original Aquila optimizer. The Aquila optimizer is reported in 2021 by Abualigah et. al. is a popular algorithm and based on the inherent behavior of Aquila to catch the pray. Simulation studies are reported for two nonlinear finite impulse response (FIR) channels performance under noisy environment. The performance is reported in the form of MSE and normalized MSE (dB) value, run time required during training; final bit error rate (BER) value obtained and BER plot during testing. Simulation results reveal superior performance of Chebyshev FLANN architecture with QAOA learning, compared to the other FLANN models as well as a FIR filter-based equalizer trained with original Aquila optimizer, Grey Wolf optimizer, particle swarm optimizer and least mean square algorithm.

Many practical engineering design problems need constrained optimization. The literature reports several meta-heuristic algorithms have been applied to solve constrained optimization problems. In many cases, the algorithms fail due to violation of constraints. Recently in 2014, a new meta-heuristic algorithm known as symbiotic organism search (SOS) is reported by Cheng and Prayogo. It is inspired by the natural phenomenon of interaction between organisms in an ecosystem which help them to survive and grow. In this paper, the SOS algorithm is combined with augmented Lagrange multiplier (ALM) method to solve the constrained optimization problems. The ALM is accurate and effective as the constraints in this case do not have the power to restrict the search space or search direction. The orthogonal array strategies have gained popularity among the meta-heuristic researchers due to its potentiality to enhance the exploitation process of the algorithms. Simultaneously, researchers are also looking at designing parallel version of the meta-heuristics to reduce the computational burden. In order to enhance the performance, an Orthogonal Parallel SOS (OPSOS) is developed. The OPSOS along with ALM method is a suitable combination which is used here to solve twelve benchmark nonlinear constrained problems and four engineering design problems. Simulation study reveals that the proposed approach has almost similar accuracy with lower run time than ALM with Orthogonal SOS. Comparative analysis also establish superior performance over ALM with orthogonal colliding bodies optimization, modified artificial bee colony, augmented Lagrangian-based particle swarm optimization and Penalty function-based genetic algorithm.

The solution of nonlinear Ordinary Differential Equations (ODE) finds potential applications in physics, economics, computing and engineering. Conventional approaches used for solving ODE are effective in case of 1st order or 2nd order problems. With increase in order the complexity associated with the problem increases. Thus instead of going for an exact solution, determining an approximate solution is also helpful. In this paper, solving ODE is handled as an optimization problem. Popular Fourier Series expansion is used as an approximation function. A hybrid algorithm Orthogonal Colliding Bodies Optimization (OCBO) is formulated by assembling good features of orthogonal array (exploration of solution in search space) and Colliding Bodies Optimization (bodies after collision quickly move to a position with minimal energy level on the search space). The coefficients of the Fourier series are computed with OCBO. Simulation studies are carried out to determine solution of popular practically used ODEs: Bernoulli Equation for flowing fluids, Integro-Differential equation, Brachistochrone equation for gravity, current response of an oscillatory Tank circuit, voltage and current decay with time in an electrical circuit. Simulations are also carried out on three benchmark ODEs used for modelling the biological processes. Comparative analysis demonstrated the superior approximation of the proposed approach over Orthogonal PSO, water cycle algorithm and Harmonic search.