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A multi-objective imperialist competitive algorithm (MOICA) for finding motifs in DNA sequences

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2019年第3期16卷 1575-1596页

作者： Gohardani, Saeed Alirezanejad Bagherian, Mehri Vaziri, Hamidreza Univ Guilan Fac Math Sci Dept Appl Math Rasht Iran Univ Guilan Fac Sci Dept Biol Rasht Iran

Motif discovery problem (MDP) is one of the well-known problems in biology which tries to find the transcription factor binding site (TFBS) in DNA sequences. In one aspect, there is not enough biological knowledge on motif sites and on the other side, the problem is NP-hard. Thus, there is not an efficient procedure capable of finding motifs in every dataset. Some algorithms use exhaustive search, which is very time-consuming for large-scale datasets. On the other side, metaheuristic procedures seem to be a good selection for finding a motif quickly that at least has some acceptable biological properties. Most of the previous methods model the problem as a single objective optimization problem;however, considering multi-objectives for modeling the problem leads to improvements in the quality of obtained motifs. Some multi-objective optimization models for MDP have tried to maximize three objectives simultaneously: Motif length, support, and similarity. In this study, the multi-objective imperialist Competition algorithm (ICA) is adopted for this problem as an approximation algorithm. ICA is able to simulate more exploration along the solution space, so avoids trapping into local optima. So, it promises to obtain good solutions in a reasonable time. Experimental results show that our method produces good solutions compared to well-known algorithms in the literature, according to computational and biological indicators.

关键词： motif discovery imperialist competitive algorithm multi-objective optimization DNA sequences transcription factor binding site

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A parallel multi-objective imperialist competitive algorithm to solve the load offloading problem in mobile cloud computing

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NEURAL COMPUTING & APPLICATIONS 2023年第26期35卷 18905-18932页

作者： Alipour, Sara Saadatfar, Hamid Poor, Mahdi Khazaie Islamic Azad Univ Comp Engn Dept Birjand Branch Birjand Iran Univ Birjand Dept Comp Engn Birjand Iran

Cloud computing is a modern architecture for performing complex and immense processes. It consists of configurable computational resource sets that communicate with each other through communication networks. With the advent of the cloud computing architecture and increasing its applications for mobile devices, the growth rate of mobile data has proliferated exponentially. Consequently, processing the tasks of mobile users has become difficult due to the limitations of these devices, such as low computing power and low capacity. Therefore, the idea of mobile cloud computing (MCC) for mobile devices using cloud-based storage and computing resources was introduced. In MCC, processing information is transferred from the user's mobile devices to the cloud servers. This process is known as the tasks offloading and scheduling of mobile users. In this case, the task execution time, CPU power consumption, network bandwidth, and task allocation time must be specified. Due to many tasks and different resources, the process of task offloading and scheduling is considered a challenging subject in the field of MCC. Therefore, in this paper, a multi-objective parallel imperialist competitive algorithm (MPICA) is proposed. The main objective of this parallel algorithm is to reduce the algorithm's execution time for searching the problem space, reducing processing time, reducing energy consumption, and improving load balance. The simulation results of the proposed algorithm represent that the parallelization of the imperialist competitive algorithm (ICA) has a significant effect on reducing the execution time of the algorithm. In general, the proposed algorithm performs better than the state-of-the-art algorithms based on the proposed criteria.

关键词： Cloud computing Mobile cloud computing Load offloading Task scheduling imperialist competitive algorithm Parallel algorithm

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Application of generalized flexibility matrix in damage identification using imperialist competitive algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第4期19卷 994-1001页

作者： Masoumi, Mehdi Jamshidi, Ehsan Bamdad, Mahdi Islamic Azad Univ Semnan Branch Dept Mech Engn Semnan 35135179 Iran Shahrood Univ Technol Sch Mech Engn Shahrood *** Iran

In this article, a new objective function which is formed by using generalized flexibility matrix is presented with the aim of solving the constrained optimization problem in damage detection procedure. The main purpose is to decrease the effects of truncation error which ensue from cutting off the higher order modes. Two different case studies are used to evaluate the proposed approach. These cases include, a 2D-frame and a Howe-Truss structure. In this study, each structure is modeled by finite element method and damages are induced by considering a reduction in the stiffness of the elements. Finally, the inverse problem of damage identification is solved by using the imperialist competitive algorithm (ICA) for each damage scenario. Not only the effect of noise on the obtained results is studied but also a comparison is drawn between the proposed method based on ICA and other conventional optimization algorithms, namely PSO and GA.

关键词： generalized flexibility matrix imperialist competitive algorithm damage detection

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A modified imperialist competitive algorithm for multi-robot stick-carrying application

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ROBOTICS AND AUTONOMOUS SYSTEMS 2016年 76卷 15-35页

作者： Sadhu, Arup Kumar Rakshit, Pratyusha Konar, Amit Jadavpur Univ Elect & Telecommun Engn Dept Kolkata 700032 India

The paper proposes a novel evolutionary optimization approach of solving a multi-robot stick-carrying problem. The problem refers to determine the time-optimal trajectory of a stick, being carried by two robots, from a given starting position to a predefined goal position amidst static obstacles in a robot world-map. The problem has been solved using a new hybrid evolutionary algorithm. Hybridization, in the context of evolutionary optimization framework, refers to developing new algorithms by synergistically combining the composite benefits of global exploration and local exploitation capabilities of different ancestor algorithms. The paper proposes a novel approach to embed the motion dynamics of fireflies of the Firefly algorithm (FA) into a socio-political evolution-based meta-heuristic search algorithm, known as the imperialist competitive algorithm (ICA). The proposed algorithm also uses a modified random walk strategy based on the position of the candidate solutions in the search space to effectually balance the trade-off between exploration and exploitation. Thirteen other state-of-art techniques have been used here to study the relative performance of the proposed imperialist competitive Firefly algorithm (ICFA) with respect to run-time and accuracy (offset in objective function from the theoretical optimum after termination of the algorithm). Computer simulations undertaken on a well-known set of 25 benchmark functions reveal that the incorporation of the proposed strategies into the traditional ICA makes it more efficient in both run-time and accuracy. The performance of the proposed algorithm has then finally been studied on the real-time multi-robot stick-carrying problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm based stick-carrying scheme outperforms other state-of-art techniques with respect to two standard metrics defined in the literature. The application justifies the importance of

关键词： Multi-robot stick-carrying Co-ordination imperialist competitive algorithm Firefly algorithm Khepera-II

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A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第4期14卷 227-236页

作者： Mousavirad, Seyed Jalaleddin Bidgoli, Azam Asilian Komleh, Hossein Ebrahimpour Schaefer, Gerald Univ Kashan Dept Comp & Elect Engn Kashan Iran Loughborough Univ Dept Comp Sci Loughborough Leics England

The performance of artificial neural networks (ANNs) is largely dependent on the success of the training process. Gradient descent-based methods are the most widely used training algorithms but have drawbacks such as ending up in local minima. One approach to overcome this is to use population-based algorithms such as the imperialist competitive algorithm (ICA) which is inspired by the imperialist competition between countries. In this paper, we present a new memetic approach for neural network training to improve the efficacy of ANNs. Our proposed approach - memetic imperialist competitive algorithm with chaotic maps (MICA-CM) - is based on a memetic ICA and chaotic maps, which are responsible for exploration of the search space, while back-propagation is used for an effective local search on the best solution obtained by ICA. Experimental results confirm our proposed algorithm to be highly competitive compared to other recently reported methods.

关键词： neural network training imperialist competitive algorithm memetic computing chaotic map back-propagation bio-inspired computation

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An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm

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JOURNAL OF SUPERCOMPUTING 2020年第8期76卷 6302-6329页

作者： Kashikolaei, Seyedeh Monireh Ggasemnezhad Hosseinabadi, Ali Asghar Rahmani Saemi, Behzad Shareh, Morteza Babazadeh Sangaiah, Arun Kumar Bian, Gui-Bin Islamic Azad Univ Dept Comp Engn Babol Branch Babol Sar Iran Islamic Azad Univ Ayatollah Amoli Branch Young Researchers & Elite Club Amol Iran Kavosh Inst Higher Educ Comp Dept Mahmood Abad Mazandaran Iran Islamic Azad Univ Dept Comp Sci & Res Branch Tehran Iran Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Cloud computing is an Internet-based approach in which all applications and files are hosted in a cloud consisting of thousands of computers that are linked in complex ways. The major challenge of cloud data centers is to show how the millions of requests of final users are correctly and effectively being investigated and serviced. Load-balancing techniques are needed to increase the flexibility and scalability of cloud data centers. Load-balancing technique is one of the most significant issues in the distributed computing system. Since there are large-scale resources and a lot of user demands in cloud computing load-balancing problem, it could be the main reason that many researchers considered and addressed that as an NP-hard problem. Therefore, some heuristics algorithms such as imperialist competitive algorithm (ICA) and firefly algorithm (FA) had been proposed by previous researchers to solve the mentioned problem. Although ICA and FA could get an approximate satisfying result in solving the cloud computing load-balancing problem, obtaining the better result means to make improvements in makespan, CPU time, load balancing, stability and planning speed. The motivation of this research is proposing an intelligent meta-heuristic algorithm based on the combination of ICA and FA to get the mentioned required result. Local search ability of FA can reinforce ICA algorithm. The obtained result of this research showed dramatic improvements in makespan, CPU time, load balancing, stability and planning speed.

关键词： Cloud computing Task scheduling Load balancing imperialist competitive algorithm Firefly algorithm

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Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

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ENGINEERING COMPUTATIONS 2018年第4期35卷 1774-1787页

作者： Behzadafshar, Katayoun Mohebbi, Fahimeh Soltani Tehrani, Meharn Hasanipanah, Mahdi Tabrizi, Omid Islamic Azad Univ Dept Phys Coll Basic Sci Yadegar E Imam Khomeini RAHShahre Rey Branch Tehran Iran Univ Tehran Fac New Sci & Technol Tehran Iran Islamic Azad Univ Najafabad Branch Dept Civil Engn Najafabad Iran Univ Kashan Dept Min Engn Kashan Iran Islamic Azad Univ Young Researchers & Elite Club Sci & Res Branch Tehran Iran

Purpose The purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran. Design/methodology/approach For this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models' input, and the peak particle velocity (PPV) parameter was used as the models' output. Findings After modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with the R2 of 0.939 was the most precise model for predicting the PPV in the present study. Originality/value In the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

关键词： ANN Blasting imperialist competitive algorithm PPV

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Multi Case Non-Convex Economic Dispatch Problem Solving by Implementation of Multi-Operator imperialist competitive algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2017年第4期12卷 1417-1426页

作者： Eghbalpour, Hamid Nabatirad, Mohammadreza Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.

关键词： Non-convex optimization Economic dispatch Meta-heuristic algorithms imperialist competitive algorithm

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Optimization of process planning with various flexibilities using an imperialist competitive algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第5-8期59卷 815-828页

作者： Lian, Kunlei Zhang, Chaoyong Shao, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

In this paper, we investigate the optimization of process planning in which various flexibilities are considered. The objective is to minimize total weighted sum of manufacturing costs. Various flexibilities, including process flexibility, sequence flexibility, machine flexibility, tool flexibility, and tool access direction (TAD) flexibility, generally exist in process planning and consideration of these flexibilities is essential for improving production efficiency and system flexibility. However, process planning is strongly NP-hard due to the existence of various flexibilities as well as complex machining precedence constraints. To tackle this problem, an imperialist competitive algorithm (ICA) is employed to find promising solutions with reasonable computational cost. ICA is a novel socio-politically motivated metaheuristic algorithm inspired by imperialist competition. It starts with an initial population and proceeds through assimilation, position exchange, imperialistic competition, and elimination. Computational experiments on three sets of process planning problem taken from literature are carried out, and comparisons with some existing algorithms developed for process planning are presented. The results show that the algorithm performs significantly better than existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS), and particle swarm optimization (PSO).

关键词： Process planning imperialist competitive algorithm Genetic algorithm Simulated annealing

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Multi-Objective Modified imperialist competitive algorithm for Brushless DC Motor Optimization

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IETE JOURNAL OF RESEARCH 2019年第1期65卷 96-103页

作者： Sharifi, MohammadAli Mojallali, Hamed Univ Guilan Dept Elect Engn Fac Engn Rasht Iran

imperialist competitive algorithm is an evolutionary algorithm introduced for optimization problems. In this paper, multi-objective modified imperialist competitive algorithm is proposed for brushless DC motor optimization problem. In the proposed algorithm, the movement of countries toward the best imperialist is concentrated and some techniques are used to extend the single-objective algorithm to the multi-objective version. Then, the algorithm is used to optimize the design variables of brushless DC motor to maximize efficiency, minimize total mass, and satisfy six inequality constraints simultaneously. Simulation results show the superiority of the proposed algorithm over multi-objective versions of standard imperialist competitive algorithm, particle swarm optimization, improved strength Pareto evolutionary algorithm and non-dominated sorting genetic algorithm III.

关键词： Brushless DC motor imperialist competitive algorithm Multi-objective optimization

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