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Papers by Morteza Alinia-Ahandani

arXiv (Cornell University), Dec 19, 2021

The main contribution of this paper is to apply the Robust switched MPC controllers in a distribu... more The main contribution of this paper is to apply the Robust switched MPC controllers in a distributed fashion on the distributed large-scale systems with switched topology, input and state constraints, in which the subsystems interact with each other by states and inputs.

Applied Intelligence, Feb 21, 2022

2009 14th International CSI Computer Conference, Oct 1, 2009

In this paper we propose several methods for partitioning, the process of grouping members of pop... more In this paper we propose several methods for partitioning, the process of grouping members of population to different memeplexes, in a shuffled frog leaping algorithm. These proposed methods divide the population in terms of the value of cost function or the geometric position of members or quite random partitioning. The proposed methods are evaluated on several low and high dimensional benchmark functions. The obtained results on low dimensional functions demonstrate that geometric partitioning methods have the best success rate and the fastest performance. Also on high dimensional functions, however using of the geometric partitioning methods for the partitioning stage of the SFL algorithm lead to a better success rate but these methods are more time consuming than other partitioning methods.

Soft Computing, 2012

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differenti... more This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.

Computational Optimization and Applications, 2014

… Conference, 2009

Page 1. Job-Shop Scheduling Using Hybrid Shuffled Frog Leaping M. Alinia Ahandani University of T... more Page 1. Job-Shop Scheduling Using Hybrid Shuffled Frog Leaping M. Alinia Ahandani University of Tabriz, Tabriz, Iran. morteza.alinia@gmail.com ... To solve this problem, we propose two types of hybrid shuffled frog leaping algorithm. ...

Nonlinear Analysis: Hybrid Systems

Artificial Intelligence Review

Artificial Intelligence Review

Journal of Experimental & Theoretical Artificial Intelligence

Artificial Intelligence Review, 2014

Wseas Transactions on Computers, 2013

Journal of Circuits, Systems and Computers, 2014

In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WL... more In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WLAN applications which provides high dynamic range with relatively low power consumption. First, the differential evolution algorithm was used to optimize the width of input transistors, then the tunable on-chip switching stage method was applied to control the amplifier gain when the input signal increases. The optimization was performed in terms of gain, noise figure (NF), IIP3 and power dissipation. The LNA has achieved a variable gain from 16.55 to 20.45 dB with excellent NF between 1.63 and 1.74 dB. Furthermore, the proposed circuit achieves a third order input intercept point of 6.6 dBm. It consumes only 10 mW from a 1.5 V supply.

Swarm and Evolutionary Computation, 2012

International Journal of Applied Evolutionary Computation, 2014

In this research, a study was carried out to exploit the hybrid schemes combining two classical l... more In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous benchmark functions and the obtained results are compared with their non-hybrid competitors. The obtained results demonstrate that the hybrid algorithm combined from shuffled frog leaping and Nelder–Mead simplex has a better success rate but a higher number of function evaluations on low-dimensional functions than the shuffled frog leaping. Whereas on high-dimensional functions it has a better succe...

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE), 2011

The parameter identification problem can be modeled as a non-linear optimization problem. In this... more The parameter identification problem can be modeled as a non-linear optimization problem. In this problem, some unknown parameters of a mathematical model presented by an ordinary differential equation using some experimental data must be estimated. This paper presents a shuffled frog leaping algorithm for solving parameter identification problem. An opposition-based initialization strategy is used to choose the fitter members as initial population. Two test cases are considered to examine the efficiency of utilized algorithm. The comparison results of shuffled frog leaping and other methods proposed in the different literature demonstrate that the shuffled frog leaping has a comparable performance than other evolutionary algorithms.

Applied Mathematics and Computation, 2014

Information Sciences, 2015

arXiv (Cornell University), Dec 19, 2021

The main contribution of this paper is to apply the Robust switched MPC controllers in a distribu... more The main contribution of this paper is to apply the Robust switched MPC controllers in a distributed fashion on the distributed large-scale systems with switched topology, input and state constraints, in which the subsystems interact with each other by states and inputs.

Applied Intelligence, Feb 21, 2022

2009 14th International CSI Computer Conference, Oct 1, 2009

In this paper we propose several methods for partitioning, the process of grouping members of pop... more In this paper we propose several methods for partitioning, the process of grouping members of population to different memeplexes, in a shuffled frog leaping algorithm. These proposed methods divide the population in terms of the value of cost function or the geometric position of members or quite random partitioning. The proposed methods are evaluated on several low and high dimensional benchmark functions. The obtained results on low dimensional functions demonstrate that geometric partitioning methods have the best success rate and the fastest performance. Also on high dimensional functions, however using of the geometric partitioning methods for the partitioning stage of the SFL algorithm lead to a better success rate but these methods are more time consuming than other partitioning methods.

Soft Computing, 2012

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differenti... more This paper proposes using the opposition-based learning (OBL) strategy in the shuffled differential evolution (SDE). In the SDE, population is divided into several memeplexes and each memeplex is improved by the differential evolution (DE) algorithm. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the DE which, on one hand, use the partitioning and shuffling concepts of SDE to compensate for the limited amount of search moves of the original DE and, on the other hand, employ the OBL to accelerate the DE without making premature convergence. Four versions of DE algorithm are proposed based on the OBL and SDE strategies. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments on 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005 and non-parametric analysis of obtained results demonstrate that the performances of the proposed algorithms are better than the SDE. The fourth version of proposed algorithm has a significant difference compared to the SDE in terms of all considered aspects. The emphasis of comparison results is to obtain some successful performances on unsolved functions for the first time, which so far have not been reported any successful runs on them. In a later part of the comparative experiments, performance comparisons of the proposed algorithm with some modern DE algorithms reported in the literature confirm a significantly better performance of our proposed algorithm, especially on high-dimensional functions.

Computational Optimization and Applications, 2014

… Conference, 2009

Page 1. Job-Shop Scheduling Using Hybrid Shuffled Frog Leaping M. Alinia Ahandani University of T... more Page 1. Job-Shop Scheduling Using Hybrid Shuffled Frog Leaping M. Alinia Ahandani University of Tabriz, Tabriz, Iran. morteza.alinia@gmail.com ... To solve this problem, we propose two types of hybrid shuffled frog leaping algorithm. ...

Nonlinear Analysis: Hybrid Systems

Artificial Intelligence Review

Artificial Intelligence Review

Journal of Experimental & Theoretical Artificial Intelligence

Artificial Intelligence Review, 2014

Wseas Transactions on Computers, 2013

Journal of Circuits, Systems and Computers, 2014

In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WL... more In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WLAN applications which provides high dynamic range with relatively low power consumption. First, the differential evolution algorithm was used to optimize the width of input transistors, then the tunable on-chip switching stage method was applied to control the amplifier gain when the input signal increases. The optimization was performed in terms of gain, noise figure (NF), IIP3 and power dissipation. The LNA has achieved a variable gain from 16.55 to 20.45 dB with excellent NF between 1.63 and 1.74 dB. Furthermore, the proposed circuit achieves a third order input intercept point of 6.6 dBm. It consumes only 10 mW from a 1.5 V supply.

Swarm and Evolutionary Computation, 2012

International Journal of Applied Evolutionary Computation, 2014

In this research, a study was carried out to exploit the hybrid schemes combining two classical l... more In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous benchmark functions and the obtained results are compared with their non-hybrid competitors. The obtained results demonstrate that the hybrid algorithm combined from shuffled frog leaping and Nelder–Mead simplex has a better success rate but a higher number of function evaluations on low-dimensional functions than the shuffled frog leaping. Whereas on high-dimensional functions it has a better succe...

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE), 2011

The parameter identification problem can be modeled as a non-linear optimization problem. In this... more The parameter identification problem can be modeled as a non-linear optimization problem. In this problem, some unknown parameters of a mathematical model presented by an ordinary differential equation using some experimental data must be estimated. This paper presents a shuffled frog leaping algorithm for solving parameter identification problem. An opposition-based initialization strategy is used to choose the fitter members as initial population. Two test cases are considered to examine the efficiency of utilized algorithm. The comparison results of shuffled frog leaping and other methods proposed in the different literature demonstrate that the shuffled frog leaping has a comparable performance than other evolutionary algorithms.

Applied Mathematics and Computation, 2014

Information Sciences, 2015