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Mirjalili S.,Griffith University | Mirjalili S.,Queensland Institute of Business and Technology | Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Hatamlou A.,Islamic Azad University at Khoy
Neural Computing and Applications | Year: 2016

This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html. © 2015, The Natural Computing Applications Forum.


Abedifar V.,Shahid Beheshti University | Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Eshghi M.,Shahid Beheshti University
Optical Fiber Technology | Year: 2015

In all-optical networks the ASE noise of the utilized optical power amplifiers is a major impairment, making the OSNR to be the dominant parameter in QoS. In this paper, a two-objective optimization scheme using Multi-Objective Particle Swarm Optimization (MOPSO) is proposed to reach the maximum OSNR for all channels while the optical power consumed by EDFAs and lasers is minimized. Two scenarios are investigated: Scenario 1 and Scenario 2. The former scenario optimizes the gain values of a predefined number of EDFAs in physical links. The gain values may be different from each other. The latter scenario optimizes the gains value of EDFAs (which is supposed to be identical in each physical link) in addition to the number of EDFAs for each physical link. In both scenarios, the launch powers of the lasers are also taken into account during optimization process. Two novel encoding methods are proposed to uniquely represent the problem solutions. Two virtual demand sets are considered for evaluation of the performance of the proposed optimization scheme. The simulations results are described for both scenarios and both virtual demands. © 2014 Elsevier Inc. All rights reserved.


Saremi S.,Griffith University | Saremi S.,Queensland Institute of Business and Technology | Mirjalili S.Z.,Zharfa Pajohesh System ZPS Co. | Mirjalili S.M.,Zharfa Pajohesh System ZPS Co.
Neural Computing and Applications | Year: 2015

Evolutionary population dynamics (EPD) deal with the removal of poor individuals in nature. It has been proven that this operator is able to improve the median fitness of the whole population, a very effective and cheap method for improving the performance of meta-heuristics. This paper proposes the use of EPD in the grey wolf optimizer (GWO). In fact, EPD removes the poor search agents of GWO and repositions them around alpha, beta, or delta wolves to enhance exploitation. The GWO is also required to randomly reinitialize its worst search agents around the search space by EPD to promote exploration. The proposed GWO–EPD algorithm is benchmarked on six unimodal and seven multi-modal test functions. The results are compared to the original GWO algorithm for verification. It is demonstrated that the proposed operator is able to significantly improve the performance of the GWO algorithm in terms of exploration, local optima avoidance, exploitation, local search, and convergence rate. © 2014, The Natural Computing Applications Forum.


Mirjalili S.Z.,Zharfa Pajohesh System ZPS Co. | Saremi S.,Griffith University | Saremi S.,Queensland Institute of Business and Technology | Mirjalili S.M.,Zharfa Pajohesh System ZPS Co.
Neural Computing and Applications | Year: 2015

Training feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified by the comparison with five other well-known heuristics. The results prove that the proposed FNNSSO is able to provide very promising results compared with other algorithms. © 2015, The Natural Computing Applications Forum.


Mirjalili S.,Griffith University | Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Lewis A.,Griffith University
Information Sciences | Year: 2014

The Multi-Layer Perceptron (MLP), as one of the most-widely used Neural Networks (NNs), has been applied to many practical problems. The MLP requires training on specific applications, often experiencing problems of entrapment in local minima, convergence speed, and sensitivity to initialization. This paper proposes the use of the recently developed Biogeography-Based Optimization (BBO) algorithm for training MLPs to reduce these problems. In order to investigate the efficiencies of BBO in training MLPs, five classification datasets, as well as six function approximation datasets are employed. The results are compared to five well-known heuristic algorithms, Back Propagation (BP), and Extreme Learning Machine (ELM) in terms of entrapment in local minima, result accuracy, and convergence rate. The results show that training MLPs by using BBO is significantly better than the current heuristic learning algorithms and BP. Moreover, the results show that BBO is able to provide very competitive results in comparison with ELM. © 2014 Elsevier Inc. All rights reserved.


Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Mirjalili S.Z.,Zharfa Pajohesh System ZPS Co.
Infrared Physics and Technology | Year: 2015

This work designs a type of line-defect photonic crystal waveguide (PCW) called hypoellipse PCW (HPCW) that considers two conflicting issues: group index and bandwidth. To do so, the recent multi objective framework called MoMIR is employed. A wide range of designs obtained demonstrates the advantage of considering group index and bandwidth simultaneously when designing HPCWs. Comparison of the proposed HPCW with the current best PCWs shows a nearly 7% improvement over the latter in terms of normalized delay-bandwidth product (NDBP). Analysis of the results reveals some of the physical rules about the structure of the HPCW. Finally, optical pulse propagation in obtained HPCWs and the process of designing an optical buffer by using an obtained design are explained. © 2015 Elsevier B.V.


Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Mirjalili S.,Zharfa Pajohesh System ZPS Co.
IEEE Photonics Technology Letters | Year: 2014

This letter proposes a new type of photonic crystal waveguide (PCW) called oval-shaped-hole PCW (OPCW). In the proposed structure, the circle-shaped holes are replaced by oval-shaped holes. In order to find the best possible structural parameters, the recently proposed multiobjective framework called MoMIR is employed. The calculation results show that the proposed optimized OPCWs are able to outperform the best current PCW in the literature significantly. © 1989-2012 IEEE.


Mirjalili S.M.,Zharfa Pajohesh System ZPS Co. | Mirjalili S.Z.,Zharfa Pajohesh System ZPS Co.
Neural Computing and Applications | Year: 2016

This work presents a single-objective optimization framework for designing complex photonic crystal (PhC) filters. As a case study, a super defect PhC filter with five rods is considered. Due to the large number of structural parameters and complexity of designing process, the problem is formulated and optimized by using a recent optimization algorithm called multi-verse optimizer (MVO). Six optimal super defect filters are obtained by MVO with respect to the WDM standard, which is defined by ITU-T Recommendation G.694.2. The designed super defect filters are then placed side by side to implement WDM. The results of FDTD simulation of the designed WDM show that the magnitude of output spectral transmission is higher than that of the current works in the literature. In addition, the high-quality factor and low crosstalk value (−32.9 dB) are the other advantages of the designed WDM with optimal super defect filters. © 2016 The Natural Computing Applications Forum


Mirjalili S.M.,Zharfa Pajohesh System ZPS Co.
Applied Optics | Year: 2014

This work proposes a modularized framework for designing the structure of photonic crystal waveguides (PCWs) and reducing human involvement during the design process. The proposed framework consists of three main modules: parameters module, constraints module, and optimizer module. The first module is responsible for defining the structural parameters of a given PCW. The second module defines various limitations in order to achieve desirable optimum designs. The third module is the optimizer, in which a numerical optimization method is employed to perform optimization. As case studies, two new structures called Ellipse PCW (EPCW) and Hypoellipse PCW (HPCW) with different shape of holes in each row are proposed and optimized by the framework. The calculation results show that the proposed framework is able to successfully optimize the structures of the new EPCW and HPCW. In addition, the results demonstrate the applicability of the proposed framework for optimizing different PCWs. The results of the comparative study show that the optimized EPCW and HPCW provide 18% and 9% significant improvements in normalized delay-bandwidth product (NDBP), respectively, compared to the ring-shape-hole PCW, which has the highest NDBP in the literature. Finally, the simulations of pulse propagation confirm the manufacturing feasibility of both optimized structures. © 2014 Optical Society of America.


Mirjalili S.M.,Zharfa Pajohesh System ZPS Co | Mirjalili S.,Zharfa Pajohesh System ZPS Co | Mirjalili S.Z.,Zharfa Pajohesh System ZPS Co
Electronics Letters | Year: 2015

The use of multi-objective optimisation techniques in the field of artificial intelligence for designing photonic crystal (PhC) light-emitting diodes (LEDs) is proposed. The extraction ratio and Purcell factor are first identified and considered as the main objectives when designing such LEDs. These two objectives are then optimised simultaneously even though both are in conflict. The proposed method is developed within a framework and applied to a case study. A comparative study shows that the optimised PhC LEDs completely outperform current works in the literature. These results are obtained by just tuning the PhC structural parameters, which can be considered as a substantial achievement in this field. © 2015 The Institution of Engineering and Technology.

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