Ali Mohammadi's repositories
Design-and-modeling-of-adaptive-IIR-filtering-systems-using-a-weighted-sum-variable-length-PSO
Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length particle swarm optimization
Design-of-optimal-CMOS-ring-oscillator-using-an-intelligent-optimization-tool
This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.
IPO-Inclined-Planes-system-Optimization-Algorithm
A new optimization method based on the dynamic of sliding motion along a frictionless inclined plane. In IPO, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton’s second law and equations of motion. The standard version of the IPO is presented by Mozafari et al. in 2016. Powerful improved versions of it called MIPO and SIPO along with its multi-objective version of MOIPO were presented in 2016, 2017 and 2019 by Dr. Ali Mohammadi (myself) and colleagues at the University of Birjand, respectively. This powerful algorithm has also been used in many applications, which has provided very good outputs. In the following, the standard version of the IPO algorithm along with the benchmark functions reviewed in its reference article, and its improved versions are attached.
A-Modified-Inclined-Planes-system-Optimization-MIPO-Algorithm
With the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors.
Designing-INS-GNSS-integrated-navigation-systems-by-using-IPO-algorithms
In this paper, an attempt is made to leverage on novel metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, a simplified version of the inclined planes system optimization (called SIPO) algorithm
Inclined-planes-system-optimization-theory-literature-review-and-state-of-the-art-versions-for-II
The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton’s second law to perform optimization. After conducting a thorough literature review, this paper proposes an improved version of IPO called IIPO. This improvement is achieved by changing exploratory and exploitative behavior of the standard IPO proportional to the progress of optimization (iteration). The IIPO is employed for optimizing IIR digital filter design, which is a challenging engineering problem. Adaptive IIR modeling as a multimodal optimization problem is designed and developed under system identification structure with an appropriate single-objective function in the frequency domain. Implementations are performed in both modeling cases with same and reduced orders, and under two identification forms with and without environmental additive noise. The results are reported along with various analyzes compared to a wide range of IPO variants. The statistical results on 100 independent trials show a success of more than 90% of cases, the proposed IIPO algorithm substantially outperforms other comparative algorithms in terms of accuracy of estimated coefficients, convergence, fitness, output responses, noise analysis, stability, and reliability.https://doi.org/10.1016/j.eswa.2022.117127
A-Simplified-and-Efficient-Version-of-Inclined-Planes-system-Optimization-SIPO-Algorithm
A simplified and effective version of IPO (called SIPO) with the aim of simplifying the main IPO equations, creating a powerful trade-off between the concepts of exploitation and exploration, and modifying the complexity of their structural parameters.
Intelligent-Optimization-Literature-Review-and-State-of-the-Art-Algorithms-1965-2022-
This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965-2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories.
Multi-Objective-Inclined-Planes-system-Optimization-MOIPO-
Multi-objective optimization based on sloping plate optimization algorithm called Multi-objective Inclined Planes system optimization algorithm (MOIPO) is presented in this link. The proposed method uses the concept of Pareto optimization to identify non-dominant positions and an external tank to maintain these positions.
Nature-Inspired-Metaheuristic-Search-Algorithms-for-Optimizing-Benchmark-Problems-Inclined-Planes-S
In the literature, different types of inclined planes system optimization (IPO) algorithms have been proposed and evaluated in various applications. Due to the large number of variants and applications, this work provides an overview of IPO’s state-of-the-art in terms of variants presented, applications, statistical evaluation, and analysis. In addition, the performance of IPO variants are evaluated and compared. The results are benchmarked against other algorithms. Final evaluation based on statistical analysis and a new and effective ranking methodology indicates the optimal performance and relative success of all IPO variants and their performance in comparison with other recent diverse metaheuristic search competitors, including reinforcement learning, evolution-based, swarm-based, physics-based, and human-based. The performance of IPO variants shown that the use of bio-operators to improve the standard version is more successful than other applied approaches. So that, the successful performance of SIPO + M with a minimum overall ranking of 0.73 has been ahead of all versions, and the complexity of IPO equations has also been led to a high time loss and achieving a maximum overall ranking of 2.07. Among other algorithms, it shown that versions without control parameters perform exploration and exploitation processes intelligently and more successful. For example, POA-I, POA-II, SLOA, OPA, and CMBO are among the methods that achieved the best performance, with minimum overall ranking values of 0.363, 0.384, 0.387, 0.424, and 0.933, respectively.