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Combining genetic local search into a multi-population imperialist competitive algorithm for the Capacitated Vehicle Routing Problem

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APPLIED SOFT COMPUTING 2023年 142卷

作者： Rezaei, Babak Guimaraes, Frederico Gadelha Enayatifar, Rasul Haddow, Pauline C. Univ Fed Minas Gerais UFMG Dept Elect Engn Belo Horizonte MG Brazil Islamic Azad Univ Firoozkooh Branch Dept Comp Engn Firoozkooh Iran Norwegian Univ Sci & Technol NTNU Dept Comp Sci Trondheim Norway

The Vehicle Routing Problem (VRP) is one of the most significant problems in operational research today. VRP has a vast range of application fields such as transportation, logistics, manufacturing, relief systems and communication. To suit the needs of different real-world VRP scenarios, many models of VRP have been developed - CVRP (Capacitated VRP) being the classical form. In this article, a hybrid metaheuristic algorithm, ICAHGS, is proposed for solving CVRP. The present study proposes a refined imperialist competitive algorithm (ICA) as the primary evolutionary and multipopulation method for addressing the Capacitated Vehicle Routing Problem (CVRP). In order to further optimize the search process, a Hybrid Genetic Search (HGS-CVRP) algorithm is applied as an enhanced local search and population management strategy within the ICA framework. Additionally, the internal restart step of the HGS-CVRP algorithm is replaced with a multi-step restart mechanism for intensification improvement. One notable aspect of the proposed method is its ability to facilitate parallel processing, with each empire able to be processed on a separate processor. This structure allows for increased computational efficiency in addressing the CVRP. To assess the effectiveness of the proposed algorithm, it has been compared to several state-of-the-art algorithms from the literature. The results of this comparison, which include both classical benchmark instances and real-world applications, demonstrate the competitive performance of the proposed algorithm.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Vehicle Routing Problem Evolutionary computation imperialist competitive algorithm Hybrid Genetic Search

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OPTIMIZATION OF PRODUCT LINE CONSIDERING COMPATIBILITY AND RELIABILITY VIA DISCRETE imperialist competitive algorithm

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RAIRO-OPERATIONS RESEARCH 2021年第6期55卷 3773-3795页

作者： Liu, Chunfeng Yang, Xiao Wang, Jufeng Hangzhou Dianzi Univ Sch Management Hangzhou 310018 Peoples R China China Jiliang Univ Dept Math Hangzhou 310018 Peoples R China

In the era of mass customization, designing optimal products is one of the most critical decision-making for a company to stay competitive. More and more customers like customized products, which will bring challenges to the product line design and the production. If a company adopts customers' favorite levels, this may lead to lower product reliability, or incompatibility among the components that make up the product. Moreover, it is worth outsourcing certain attribute levels to reduce production cost, but customers may dislike these levels because of their delivery delay. If managers consider the compatibility issue, the quality issue, outsource determination, and the delivery due date in the product design and production stages, they will avoid unreasonable product configuration and many unnecessary expenses, thereby bringing benefits to the company. To solve this complicated problem, we establish a nonlinear program that maximizes Per-capita-contribution Margin considering Reliability Penalty. Since the integrated product line design and production problem is NP-hard, we propose an improved Discrete imperialist competitive algorithm (DICA). The proposed DICA is compared with genetic algorithm (GA) and simulated annealing (SA) through extensive numerical experiment, and the results show that DICA displays 6%similar to 17% and 5%similar to 14% improvement over GA and SA in terms of solution quality, respectively.

关键词： Mass customization product line optimization imperialist competitive algorithm compatibility product reliability

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A Modified imperialist competitive algorithm for Spoofing Attack Detection in Single-Frequency GPS Receivers

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期119卷 919-940页

作者： Shafiee, E. Mosavi, M. R. Moazedi, M. Iran Univ Sci & Technol Dept Elect Engn Tehran *** Iran Univ Mohaghegh Ardabili Fac Adv Technol Dept Engn Sci Namin Iran

A GPS spoofing attack broadcasts counterfeit signals to resemble standard GPS satellite signals to take control of the correlation peaks of GPS signals to force the victim receiver to estimate a false position. The proposed approach is based on an intelligent dynamic algorithm focusing on distinguishing authentic and counterfeit signals. This paper presents a method based on the imperialist competitive algorithm to investigate correlation peaks of authentic and counterfeit signals. In this method, a feature vector is derived from the correlation function. When the spoofing signal tries to take control of the tracking loop, the feature values deviate. Accordingly, the cluster size of the spoofing signal increases until it becomes equal to or greater than the authentic signal size, and thus, the spoofing signal is detected. The performance of the presented detection approach is validated through simulations on real data. It reveals that the proposed method can detect spoofing attacks in 99.7% of cases.

关键词： GPS Spoofing Detection imperialist competitive algorithm

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Ranking of decision making units using the imperialist competitive algorithm in DEA

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MEASUREMENT & CONTROL 2021年第9-10期54卷 1326-1335页

作者： Keshteli, Hasan Babaei Rostamy-Malkhalifeh, Mohsen Lotfi, Farhad Hosseinzadeh Islamic Azad Univ Dept Math Sci & Res Branch Tehran *** Iran

One of the challenging and important subjects in Data Envelopment Analysis (DEA) is the ranking of Decision Making Units (DMUs). In this paper, a new method for ranking the efficient DMUs is firstly proposed by utilizing the DEA technique and also developing a capable metaheuristic, imperialist competitive algorithm, derived from social, political, and cultural phenomena. Efficient DMUs are known as colonizers, and the virtual units, which are within their regions of exclusive domination, are considered as colonies. Efficient units are ranked by utilizing the factor of competition among imperialists to attract each other's colonies. One advantage of proposed method is that, without solving any mathematical, and complex solution approaches, all extreme and non-extreme units are ranked only by comparing the pairs.

关键词： Ranking data envelopment analysis imperialist competitive algorithm region of domination

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An efficient imperialist competitive algorithm with likelihood assimilation for topology, shape and sizing optimization of truss structures

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APPLIED MATHEMATICAL MODELLING 2021年 93卷 1-27页

作者： Dehghani, Milad Mashayekhi, Mostafa Sharifi, Mehdi Univ Qom Fac Engn Dept Civil Engn Qom Iran Vali E Asr Univ Rafsanjan Dept Civil Engn Fac Engn Rafsanjan Iran

This article presents an efficient hybrid meta-heuristic algorithm for topology, layout and sizing optimization of truss structures. A new assimilation scheme is implemented in the imperialist competitive algorithm (ICA) in order to improve computational efficiency, the likelihood of occurrence and the neighborhood patterns are used, and the assimilation step of the ICA is enhanced. In this method, the probabilities are assigned to each alternative by the imperialist and its neighbors in the search space;then, the colonies construct new solutions (moving to the relevant imperialist) based on the likelihood of occurrence. Neighborhood patterns are proposed to gather information from the neighboring countries in order to extract features based on the local power variation. In this study, the extended abilities of the proposed algorithm are inspired from the dolphin echolocation (DE) algorithm and the cellular automata (CA) method, which the new algorithm is denoted as CA-ICEA. The optimization results obtained by ICA, DE and CA-ICEA methods are compared. Remarkably, the proposed algorithm outperforms its competitors in terms of optimum weights, their mean and standard deviation. (c) 2020 Elsevier Inc. All rights reserved.

关键词： imperialist competitive algorithm Cellular automata Dolphin echolocation Double layer grids Topology optimization Layout optimization

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imperialist competitive algorithm applied to the optimal integration of photovoltaic distributed generation units into a microgrid

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2022年 2卷

作者： Alvarado-Reyes, Sergio Villar-Yacila, Paul Fiestas, Hugo Department of Electric-Mechanical Engineering Universidad de Piura Piura Peru

The present article aims to demonstrate that with an algorithm based on imperialist competitive algorithm (ICA) the distributed generations of an electrical microgrid can be optimized through the connection or disconnection of small photovoltaic generations, having as its main objective to minimize electrical losses. Likewise, it aims to show that with ICA-based better results are obtained than even with the most used metaheuristic algorithms such as PSO. For this, a microgrid based on IEEE 37-bus feeder was modeled, where photovoltaic generations units were connected in 25 of its 37 buses. The modeling of the microgrid was done in OpenDSS. ICA-based and PSO algorithms were developed in MATLAB. Finally, results show that with ICA-based is possible to optimize electric scenarios. Then, although its reduction of electrical losses is as good as PSO one, ICA cloud shows greater accuracy and efficient due to the drastic decrease in computational resources required. © 2022

关键词： Distributed generation imperialist competitive algorithm Microgrid optimization PSO Renewable energy

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A Modification of the imperialist competitive algorithm With Hybrid Methods for Constrained Optimization Problems

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IEEE ACCESS 2021年 9卷 161745-161760页

作者： Luo, Jianfu Zhou, Jinsheng Jiang, Xi China Univ Geosci Sch Econ & Management Beijing 100083 Peoples R China

This paper studies a modification of the imperialist competitive algorithm to solve constrained optimization problems with hybrid methods. The imperialist competitive algorithm is a kind of evolutionary algorithm based on the colonial competition mechanism of imperialism, which is a type of social heuristic optimization algorithm. However, this algorithm needs to be modified because of some problems, including the decreasing number of empires, which leads to easily falling into a local optimum, and a lack of information exchange among countries leading to the search ability being insufficiently strong. To solve these problems and improve the search ability of the algorithm, a newly improved imperialist competitive algorithm with hybrid methods is proposed based on the imperialist competitive algorithm in this paper. In this algorithm, a crossover mechanism is introduced as the empire interaction to boost the information exchange among countries. Two groups of combinatorial operators are constructed in the process of empire assimilation and revolution to improve the algorithm's global and local search ability. Moreover, a step of empire splitting is added based on the imperialist competitive algorithm in which the number of empires is decreased rapidly by empire competition in imperialist competitive algorithm, which leads to a decrease in the population diversity. Then, the proposed algorithm is tested on 12 benchmark functions and 4 engineering problems, and the constraints are addressed by Deb's rules. The results obtained by the proposed algorithm are compared with those of algorithms that have obtained good results in recent years, and it is shown that the proposed method obtains promising and comparable results on the constrained optimization benchmarks in terms of the solution quality and robustness.

关键词： Optimization Heuristic algorithms Statistics Sociology Benchmark testing Convergence Search problems Constrained optimization problems hybrid methods imperialist competitive algorithm

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NSICA: Multi-objective imperialist competitive algorithm for feature selection in arrhythmia diagnosis

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期161卷 107025-107025页

作者： Ayar, Mehdi Isazadeh, Ayaz Gharehchopogh, Farhad Soleimanian Seyedi, MirHojjat Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Islamic Azad Univ Dept Biomed Engn Urmia Branch Orumiyeh Iran

This study proposes a multi-objective, non-dominated, imperialist competitive algorithm (NSICA) to solve optimal feature selection problems. The NSICA is a multi-objective and discrete version of the original imperialist competitive algorithm (ICA) that utilizes the competition between colonies and imperialists to solve optimization problems. This study focused on solving challenges such as discretization and elitism by modifying the original operations and using a non-dominated sorting approach. The proposed algorithm is independent of the application, and with customization, it could be employed to solve any feature selection problem. We evaluated the algorithm's efficiency using it as a feature selection system for diagnosing cardiac arrhythmias. The Pareto optimal selected features from NSICA were utilized to classify arrhythmias in binary and multi-class forms based on three essential objectives: accuracy, number of features, and false negativity. We applied NSICA to an ECGbased arrhythmia classification dataset from the UCI machine learning repository. The evaluation results indicate the efficiency of the proposed algorithm compared to other state-of-the-art algorithms.

关键词： Multi -objective imperialist competitive algorithm Arrhythmia diagnosis Feature selection Electrocardiogram Bionic engineering

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A Novel Hybrid imperialist competitive algorithm-Particle Swarm Optimization Metaheuristic Optimization algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

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ENERGIES 2023年第19期16卷 6896-6896页

作者： Charadi, Ssadik Chakir, Houssam Eddine Redouane, Abdelbari El Hasnaoui, Abdennebi El Bhiri, Brahim Mohammed V Univ Mohammadia Sch Engineers EMI Elect Power & Control Team Rabat 10000 Morocco Moroccan Sch Engn Sci EMSI SMARTiLab Rabat 10000 Morocco Hassan II Univ EEIS Lab ENSET Mohammedia Casablanca 20000 Morocco Ecole Mines Rabat ENSMR Electromech Dept Rabat 10000 Morocco

The integration of renewable sources and energy storage in residential microgrids offers energy efficiency and emission reduction potential. Effective energy management is vital for optimizing resources and lowering costs. In this paper, we propose a novel approach, combining the imperialist competitive algorithm (ICA) with particle swarm optimization (PSO) as ICA-PSO to enhance energy management. The proposed energy management system operates in an offline mode, anticipating data for the upcoming 24 h, including consumption predictions, tariff rates, and meteorological data. This anticipatory approach facilitates optimal power distribution among the various connected sources within the microgrid. The performance of the proposed hybrid ICA-PSO algorithm is evaluated by comparing it with three selected benchmark algorithms, namely the genetic algorithm (GA), ICA, and PSO. This comparison aims to assess the effectiveness of the ICA-PSO algorithm in optimizing energy management in multi-source residential microgrids. The simulation results, obtained using Matlab 2023a, provide clear evidence of the effectiveness of the hybrid ICA-PSO algorithm in achieving optimal power flows and delivering substantial cost savings. The hybrid algorithm outperforms the benchmark algorithms with cost reductions of 4.47%, 14.93%, and 26% compared to ICA, PSO, and GA, respectively. Furthermore, it achieves a remarkable participation rate of 50.6% for renewable resources in the energy mix, surpassing the participation levels of the ICA (42.88%), PSO (40.51%), and GA (38.95%). This research contributes to the advancement of power flow management techniques in the context of multi-source residential microgrids, paving the way for further research and development in this field.

关键词： imperialist competitive algorithm particle swarm optimization photovoltaic wind turbine energy flow management microgrids residential applications cost-effectiveness

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Using adaptive neuro-fuzzy inference system and imperialist competitive algorithm for leak detection in pipe networks

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MEASUREMENT 2023年第1期220卷

作者： Moosavian, Naser Kasaei, Maziar Roodsari, Babak K. Univ British Columbia UBC Civil Engn Fac Vancouver BC Canada Arizona State Univ Ind Engn Tempe AZ 85281 USA Syracuse Univ Civil & Environm Engn Syracuse NY USA

Finding the position and quantity of leakage in water distribution networks (WDNs) is challenging in cities with old water pipelines. Previous studies proposed physical and data-driven techniques to find the location and quantity of leakage in WDNs. Most approaches rely on large sample of measurements from the WDN which makes them impractical. We propose an efficient model incorporating Adaptive Neuro-Fuzzy Inference System (ANFIS) and imperialist competitive algorithm (ICA) techniques, to reduce the number of required samples. ANFIS approximates the locations and quantities of leakage, while the ICA corrects its estimations. Due to nonlinearity, the application of ICA alone results in long run times, while ANFIS reduces the number of decision variables for ICA, and hence the convergence rate improves. In other words, we reduce the search space leading to reduced computational time and improved accuracy. Results show that the normalized predicted leakage values had a maximum error rate of 10%.

关键词： ANFIS Hydraulic measurement imperialist competitive algorithm Leak detection Pressure Water distribution network

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