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This paper proposes an efficient design technique for two commonly used VLSI circuits, namely, CMOS current mirror load-based differential amplifier circuit and CMOS two-stage operational amplifier. The hybrid evolutionary method utilized for these optimal designs is random particle swarm optimization with differential evolution (RPSODE). Random PSO utilizes the weighted particles for monitoring the search directions. DE is a robust evolutionary technique. It has demonstrated an exclusive performance for the optimization problems which are continuous and global but suffers from the uncertainty issues. PSO is a robust optimization method but suffers from sub-optimality problem. This paper effectively hybridizes the random PSO and DE to remove the limitations related to both the techniques individually. In this paper, RPSODE is employed to optimize the sizes of the MOS transistors to reduce the overall area taken by the circuit while satisfying the design constraints. The results obtained from RPSODE technique are validated in SPICE environment. SPICE-based simulation results justify that RPSODE is a much better technique than other formerly reported methods for the designs of the above mentioned circuits in terms of MOS area, gain, power dissipation, etc.

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.

We show here that, in the context of Einstein's static universe (ESU), the static cosmological constant Λ_s = 0. We do so by extending (and not contradicting) the ESU relationship from Λ_s = 4πρ to Λ_s = 4πρ = 0, where ρ is the ESU matter density (G = c = 1). This extension follows from the fact that the elements of the spacetime geometry depend on pressure and energy density (ρ). Note in the ΛCDM model, Λ is associated with "Dark Energy (DE)." And, if Λ would be considered as a fundamental constant, it should be zero even for a dynamic universe. In such a case, the observed accelerated expansion could be an artifact of inhomogeneity [D. L. Wiltshire, Phys. Rev. D80 (2009) 123512; E. W. Kolb, Class. Quantum. Grav.28 (2011) 164009] or large peculiar acceleration of the Milky way [C. Tasgas, Phys. Rev. D84 (2011) 063503] or extinction of light of distant supernovae [R. E. Schild and M. Dekker, Astron. Nachr.327 (2006) 729, arXiv:astro-ph/0512236]. The same conclusion has also been obtained in an independent manner [A. Mitra, JCAP03 (2013) 007, doi: 10.1088/1475-7516/2013/03/007].

Nature inspired optimization algorithms have made substantial step towards solving of various engineering and scientific real-life problems. Success achieved for those evolutionary optimization techniques are due to simplicity and flexibility of algorithm structures. In this paper, optimal set of filter coefficients are searched by the evolutionary optimization technique called Opposition-based Differential Evolution (ODE) for solving infinite impulse response (IIR) system identification problem. Opposition-based numbering concept is embedded into the primary foundation of Differential Evolution (DE) technique metaphorically to enhance the convergence speed and the performance for finding the optimal solution. The population is generated with the evaluation of a solution and its opposite solution by fitness function for choosing potent solutions for each iteration cycle. With this competent population, faster convergence speed and better solution quality are achieved. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters for mutation, crossover and selection adopted in the basic DE technique. When tested against standard benchmark examples, for same order and reduced order models, the simulation results establish the ODE as a competent candidate to others in terms of accuracy and convergence speed.

Nonextensive statistics along with network science, an emerging branch of graph theory, are increasingly recognized as potential interdisciplinary frameworks whenever systems are subject to long-range interactions and memory. Such settings are characterized by non-local interactions evolving in a non-Euclidean fractal/multi-fractal space-time making their behavior nonextensive. After summarizing the theoretical foundations from first principles, along with a discussion of entropy bifurcation and duality in nonextensive systems, we focus on selected significant astrophysical consequences. Those include the gravitational equilibria of dark matter (DM) and hot gas in clustered structures, the dark energy(DE) negative pressure landscape governed by the highest degree of mutual correlations and the hierarchy of discrete cosmic structure scales, available upon extremizing the generalized nonextensive link entropy in a homogeneous growing network.