Arezoo Modiri

Modification of Real-Number and Binary PSO Algorithms for Accelerated Convergence

Modifications in the velocity calculation of the particle swarm optimization (PSO) algorithm are proposed. The suggested modifications aim to arrive at a faster, more straightforward and still robust search procedure as compared to the conventional method. Two main factors, i.e., personal best influence and initial velocity values, are evaluated. It is shown that in problems with wide-range parameters, the effect of personal best locations is intrinsically encompassed by that of global best locations, thereby allowing for further simplification of the PSO algorithm by eliminating the factor which accounts for the personal best solutions in the velocity calculation. This simplification expedites the convergence procedure in real PSO. It is also shown that the initial velocity values can be modified to enhance the performance in terms of achieving better solution when compared with the existing algorithms, particularly in binary PSO. In order to validate the viability of the proposed procedure, the performances of the real-number and binary PSO algorithms with different velocity calculations are assessed in 1000-run sets, and pros and cons are studied. In particular, the performance of the proposed algorithm, when used to design software defined thinned array antennas, is shown to be superior to those of the existing algorithms.

Permittivity estimation for breast cancer detection using particle swarm optimization algorithm

In this paper, particle swarm optimization (PSO) algorithm is used to estimate the permittivities of the tissue layers at microwave frequency band. According to the literature, microwave radiometry (MWR) is potentially a promising cancer detection technique. In addition, breast cancer is an appropriate candidate of MWR due to the breasts exclusive physiology. Several algorithms have been evaluated for analyzing the measurement data and solving the inverse scattering problem in MWR, and different levels of accuracy have been reported. In this paper, the potential of PSO in solving this problem is demonstrated at 1–2.25 GHz. Two distinct algorithms are developed for the two considered scenarios. In the first scenario, we assume no a priori knowledge of the tissue under the test, whereas, in the second scenario, a priori knowledge is assumed. It is noteworthy that, there are only a few research articles studying PSO for permittivity estimation. However, since these studies underestimate the loss encountered by the test samples, the methods are not valid for body tissue case. Here, measurement-based loss coefficients, reported in the existing literature, are included in the calculations. It is shown that the algorithm converges relatively fast, and, distinguishes between different tissues with an acceptable accuracy.

Efficient Design of Microstrip Antennas for SDR Applications Using Modified PSO Algorithm

In this paper, several irregularly shaped microstrip antenna structures are designed using a new version of Binary Particle Swarm Optimization (BPSO) algorithm in which the velocity calculation is modified towards a more accelerated and still robust search procedure, particularly aimed for software-defined radio (SDR) applications. The optimization results are compared using both the modified and the conventional BPSO algorithms. Pros and cons are studied in terms of optimization length, convergence speed and final design conformability to desired objectives. It is depicted that the modified BPSO achieves the design criterion considerably faster than the conventional one, at the cost of slightly limiting particle exploration ability.

Inverse 4D conformal planning for lung SBRT using particle swarm optimization.

A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%-41% for D max esophagus, 31%-68% for D max spinal cord and 7%-32% for V 13 lung.

DSP Implementation of the Particle Swarm and Genetic Algorithms for Real-Time Design of Thinned Array Antennas

Efficient implementation of sophisticated algorithms on digital signal processing (DSP) chips is a key issue in the implementation of software-defined radios. Here, focusing on beamforming and using the average calculation time and hardware usage as the two indicators of efficiency, a performance comparison between two versions of binary particle swarm optimization (PSO) and genetic algorithm, as the two popular evolutionary techniques, is presented. Using our proposed multirun strategy in DSP platforms, we show that modified PSO results in a reduction by 52% and 67% in the hardware utilization and calculation time as compared to genetic algorithm and binary PSO, respectively.

Radiotherapy Planning Using an Improved Search Strategy in Particle Swarm Optimization

Objective: Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach. Methods: We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm—a popular RT optimization technique is also implemented and used. Results: The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations. Conclusion: The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO. Significance: RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

Efficient design of microstrip antennas using modified PSO algorithm

In this paper, various microstrip antennas (MAs) are designed using a new version of binary Particle Swarm Optimization (BPSO) algorithm in which the velocity calculation is modified in order to arrive at an accelerated and robust search procedure, particularly for software defined applications.

Review of breast screening: Toward clinical realization of microwave imaging

&NA; Microwave imaging (MI) technology has come a long way to introduce a noninvasive, inexpensive, fast, convenient, and safe screening tool for clinical breast monitoring. However, there is a niche between the existing understanding of MI by engineers versus clinicians. Our manuscript targets that niche and highlights the state of the art in MI technology compared to the existing breast cancer detection modalities (mammography, ultrasound, molecular imaging, and magnetic resonance). The significance of our review article is in consolidation of up‐to‐date breast clinician views with the practical needs and engineering challenges of a novel breast screening modality. We summarize breast tissue abnormalities and highlight the benefits as well as potential drawbacks of the MI as a cancer detection methodology. Our goal is to present an article that MI researchers as well as practitioners in the field can use to assess the viability of the MI technology as a competing or complementary modality to the existing means of breast cancer screening.

Improved swarm intelligence solution in large scale radiation therapy inverse planning

This study employs particle swarm optimization to solve the non-convex inverse problem of 4D stereotactic body radiation therapy planning, targeting toxicity reduction, for a right lower lobe lung tumor with motion range of 1.5cm. A novel approach is introduced to reduce the swarm search space. 90 aperture-weights are optimized using both conventional and improved PSO algorithms over 5 optimization runs per method. It is shown that, on average, the improved PSO-based plan reduces the maximum dose to heart, spinal cord and esophagus by 43%, as compared to the conventional PSO, while swarm population is cut to half.