Design of a superachromatic quarter-wave retarder for near-infrared region using flower pollination algorithm
Abstract: Phase retarders normally exhibit strong wavelength dependence. However, for use with polychromatic light, achromatic retarders, which exhibit ideally nearly identical characteristics over a wide wavelength region, are in demand. Designing such a superachromatic retarder is a challenging task for an optical system designer where retardation will be almost constant within a wide range of wavelengths. The present investigation considers a system consisting of wave plates made of different materials, which focus on near-infrared wavelength range, viz. 800 to 2000 nm. Here, a flower pollination algorithm has been used for this nonlinear optimization problem. With the help of the mentioned optimization technique, the optimal values of thicknesses of wave plates are calculated for which the system act as a quarter-wave plate. The obtained result shows a significant improvement in terms of maximum deviation of retardation for the above-mentioned wavelength range. The proposed methodology holds promise for optimizing the design of optical systems.
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TL;DR: Among the four intelligent algorithms for designing achromatic wave plate, particle swarm optimization is best, followed by ant colony optimization, simulated annealing algorithm and genetic algorithm, which shows that the method of the fixed initial points is better than the random initial points.
4 citations
TL;DR: In this article , the authors investigated the characteristics of the achromatic bandwidth of composite a chromatic quartz half-wave plates (QHWPs), three kinds of multi-element QHWP with central wavelengths at 1000-3000 nm and relative deviation of the maximum phase retardation (Δδmax) of 1-5% were discussed.
Abstract: To investigate the characteristics of the achromatic bandwidth of composite achromatic quartz half-wave plates (QHWPs), three kinds of multi-element achromatic QHWPs with central wavelengths at 1000-3000 nm and relative deviation of the maximum phase retardation (Δδmax) of 1%-5% are discussed. Based on the particle swarm optimization algorithm, the maximum bandwidth of the composite achromatic QHWPs at room temperature is obtained. The results show that the achromatic bandwidth increases with Δδmax. At the same Δδmax, the achromatic bandwidth of four-element achromatic QHWPs is larger than that of two- and three-element achromatic QHWPs. The maximum achromatic bandwidth of four-element achromatic QHWPs can reach 2229 nm when Δδmax is 5%. In addition, the temperature effect on bandwidth in the wavelength range of 300-1500 nm is analyzed, and the maximum bandwidth of temperature insensitive composite achromatic QHWPs is 840 nm. The results provide a great reference for designing achromatic wave plates with broad bandwidths.
TL;DR: In this paper , a quarter wave phase retarder having crystals of calcite, crystalline quartz and ADP materials operating over a wide range, i.e., 133 −176 THz, was designed to provide an improved achromatic behavior in terms of the deviation of retardation as compared to commercially available systems over that particular frequency range.
Abstract: A spectroscopy imaging system uses the near-infrared (NIR) to short-wave infrared (SWIR) region of the electromagnetic spectrum for applications, e.g., aerosol and atmospheric studies, and medical applications. Frequency corresponding to this wavelength range belongs to terahertz (THz). Performance of these systems depends on the degree of achromatism of phase retarders since it is an essential component that controls the phase shifts associated with incoming light waves to investigate their polarization states. The present communication deals with designing a quarter wave phase retarder having crystals of calcite, crystalline quartz and ADP materials operating over a wide range, i.e., 133–176 THz. This three-plate system provides an improved achromatic behaviour in terms of the deviation of retardation as compared to commercially available systems over that particular frequency range. Also, the system performance with respect to the variations in temperature and thickness of plates has been studied.
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IBM1
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Abstract: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to information-theoretic aspects of optimization and benchmark measures of performance are also presented. Other issues addressed include time-varying optimization problems and a priori "head-to-head" minimax distinctions between optimization algorithms, distinctions that result despite the NFL theorems' enforcing of a type of uniformity over all algorithms.
10,771 citations
TL;DR: This paper presents convergence properties of the Nelder--Mead algorithm applied to strictly convex functions in dimensions 1 and 2, and proves convergence to a minimizer for dimension 1, and various limited convergence results for dimension 2.
Abstract: The Nelder--Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization. Despite its widespread use, essentially no theoretical results have been proved explicitly for the Nelder--Mead algorithm. This paper presents convergence properties of the Nelder--Mead algorithm applied to strictly convex functions in dimensions 1 and 2. We prove convergence to a minimizer for dimension 1, and various limited convergence results for dimension 2. A counterexample of McKinnon gives a family of strictly convex functions in two dimensions and a set of initial conditions for which the Nelder--Mead algorithm converges to a nonminimizer. It is not yet known whether the Nelder--Mead method can be proved to converge to a minimizer for a more specialized class of convex functions in two dimensions.
7,141 citations
Book•
01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
3,626 citations
"Design of a superachromatic quarter..." refers background in this paper
...The main reasons behind attractiveness of metaheuristics are its simplicity, flexibility, derivation-free mechanism, and local optima avoidance capability.(11) These properties make metaheuristics highly suitable for application in real-life optimization problems....
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...Metaheuristics are one of the well-accepted subclasses of optimization techniques.(10,11) Here, optimization processes are inspired by different physical phenomena or animals’ and insects’ behaviors or evolutionary concepts....
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TL;DR: In this article, a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers, was proposed, which is more efficient than both GA and PSO.
Abstract: Flower pollination is an intriguing process in the natural world. Its evolutionary characteristics can be used to design new optimization algorithms. In this paper, we propose a new algorithm, namely, flower pollination algorithm, inspired by the pollination process of flowers. We first use ten test functions to validate the new algorithm, and compare its performance with genetic algorithms and particle swarm optimization. Our simulation results show the flower algorithm is more efficient than both GA and PSO. We also use the flower algorithm to solve a nonlinear design benchmark, which shows the convergence rate is almost exponential.
1,415 citations
TL;DR: A comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate, and the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Abstract: Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
454 citations