Multi-objective Imperialist Competitive Algorithm Research Articles

Presenting a Model for Locating and Allocating Multi-Period Hubs and Comparing It With a Multi-Objective Imperialist Competitive Algorithm

Abstract Recently, air pollution has received much attention as a result of reflections on environmental issues. Accordingly, the hub location problem (HLP) seeks to find the optimal location of hub facilities and allocate points for them to meet the demands between source-destination pairs. Thus, in this study, decisions related to location and allocation in a hub network are reviewed and a multi-objective model is proposed for locating and allocating capacity-building facilities at different time periods over a planning horizon. The objective functions of the model presented in this study are to minimize costs, reduce air pollution by diminishing fuel consumption, and maximize job opportunities. In order to solve the given model, the General Algebraic Modeling System (GAMS) along with innovative algorithms are utilized. The results presented a multi-objective sustainable model for full-covering HLP, and provided access to a hub network with minimum transport costs, fuel consumption, and GHG (greenhouse gas) emissions, and maximum job opportunities in each planning horizon utilizing MOICA (multi-objective imperialist competitive algorithm) and GAMS to solve the proposed model. The study also assessed the performance of the proposed algorithms with the aid of the QM, MID, SM, and NSP indicators, acquired from comparing the proposed meta-heuristic algorithm based on some indicators, proving the benefit and efficiency of MOICA in all cases.

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

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

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

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 ECG-based 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.

Designing a reliable aggregate production planning problem during the disaster period

The purpose of this research is to introduce a Bi-Objective (BO) model for dealing with Aggregate Production Planning (APP) for a multi-product and multi-period Supply Chain Network (SCN) that incorporates multiple suppliers, factories, and demand points. One of the goals of the model is to minimize the total cost of this network during the disaster period. The other goal is to account for probabilistic lead times to maximize the minimum level of producers' reliability during the COVID-19 pandemic. They are done to ameliorate the system's performance and improve the reliability of production plans. Finally, considering that the mentioned problem is NP-hard, a Multi-Objective Imperialist Competitive Algorithm (MOICA) based on Pareto is used to solve the proposed model, and a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is also utilized to measure the performance of the mentioned algorithm. The generated experimental problems' results demonstrate the proposed algorithm's power in finding Pareto solutions. According to innovation, this is the first paper on these topics considering the conditions of the COVID-19 disaster.

A Multi-objective Cash-in-transit Pollution-location-routing Problem Based on Urban Traffic Conditions

Cash transfer from the central treasury to the bank branches and automated teller machines (ATMs) all over the city is one of the vital processes in a banking system. There are multiple factors (e.g., location of the treasury, transportation fleet, geographic distribution of the branches and ATMs, the demand for cash, customer satisfaction, and traffic that influence the efficiency of the cash transfer). Moreover, environmental issues, and in particular the issue of greenhouse gas (GHG) emissions are given weight. In this paper, a new mathematical model for a location-routing problem with transport vehicles in the banking system is developed based on urban traffic in such a way that three objectives of decreasing greenhouse emissions, reducing location and routing costs, and increasing customer satisfaction are taken into consideration simultaneously. Furthermore, a new multi-objective genetic algorithm hybridized with a PROMETHEE method, namely the multi-objective genetic-PROMETHEE algorithm (MOGPA), is developed to tackle the proposed model. The efficiency of the proposed algorithm is examined by comparing it with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective imperialist competitive algorithm (MOICA) for the real-case issue of Saman Bank. Because management assumptions are considered in the preference functions of the proposed algorithm, the results show that the solutions of the proposed algorithm are more efficient and closer to reality.

Optimization model for designing personalized tourism packages

The tourism supply chain aims at satisfying the needs of the tourists based on their preferences. However, the preference of each tourist may be different. Some tourists prefer to optimize a single criterion, while others prefer to optimize conflicting multiple-criteria. The tourism service provider can hardly offer the tourists with the itinerary according to their precise preferences. This paper proposes a multi-objective optimization framework based on which tourists can generate itineraries according to their preferences. A mathematical model is presented, which is multi-objective and NP-hard. Consequently, four meta-heuristic algorithms, namely none-dominated sorting genetic algorithm versions II (NSGA-II) and III (NSGA-III), multi objective grey wolf optimization, and multi objective imperialist competitive algorithm are developed. The proposed method helps the tourists to compare different combinations of activities and select the one that best suits their preferences. The model is tested on a small-scale real case pertaining to the Sultanat of Oman. Thereafter, the performances of the proposed algorithms were evaluated on large scale problems. The result shows that NSGAs outperformed other algorithms. NSGA-II outperformed its NSGA-III counterpart in small instances. Surprisingly, as the size of the problem increases, the efficiency of NSGA-II decreases while that of NSGA-III increases. In large instances, NSGA-III outperformed NSGA-II.

A dynamic ensemble deep deterministic policy gradient recursive network for spatiotemporal traffic speed forecasting in an urban road network

Traffic congestion is a difficult problem that restricts the construction of urbanization. Spatiotemporal traffic speed forecasting technologies can provide effective technical support for alleviating traffic congestion and ensuring vehicle travel safety. The ensemble learning algorithm is a hot topic in traffic speed modeling. In this field, previous ensemble learning methods mainly adopt the principle of static modeling, which limits the learning ability of the model to dynamic features. To solve this problem, in this paper, a new dynamic ensemble deep deterministic policy gradient recursive network is presented for traffic speed forecasting, which comprises three main modeling steps. In step I, the simple recursive network (SRU) and temporal convolution network (TCN) methods are used as the main predictors to build the traffic speed forecasting model. In step II, the multi-objective imperialist competitive algorithm (MOICA) integrates these neural networks by optimizing the weight coefficients and generating the Pareto solution set. In step III, the deep deterministic policy gradient (DDPG) method dynamically selects the Pareto optimal solution of the MOICA according to the changes in the traffic speed data. The MOICA and DDPG dynamically integrate the forecasting results from the SRU and TCN to obtain the final results. Based on the experimental analysis results, several conclusions can be given as follows: (a) the model presented in this paper can obtain accurate traffic speed forecasting results with MAPE values below 4% on all data sets. (b) the proposed model can achieve better results than thirteen alternative models and four proposed models from other researchers. (c) the proposed model can improve the prediction performance of traditional predictors by about 6%.

An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times.

Surgical case scheduling is a key issue in the field of medician, which is a challenging work because of the difficulty in assigning resources to patients. This study regards the surgical case scheduling problem as a flexible job shop scheduling problem (FJSP). Considering the switching and preparation time of patients in different stage, an improved multi-objective imperialist competitive algorithm (IMOICA), which adopts the non-dominant sorting method, is proposed to optimize the whole scheduling. First, the social hierarchy strategy is developed to initialize the empire. Then, to enhance the global search ability of the algorithm, the concept of attraction and repulsion (AR) is introduced into the assimilation strategy. Moreover, to increase the diversity of the population, the revolution strategy is utilized. Finally, the variable neighborhood search (VNS) strategy is embedded to improve its exploitation capacity further. Experiments show that scheduling in advance saves time and cost, and IMOICA can solve the surgical case scheduling problem studied efficiently.

Optimal operation of water resources systems using MOICA algorithm with reservoir hedging approach in low‐water regions*

AbstractIn this study, simulation and optimization models are combined to apply the reservoir hedging policy. The simulation of the study basin is done by the Water Planning and Evaluation System (WEAP) model to operate the Doiraj dam reservoir located on Doiraj River. In addition, to optimize the system, the multi‐objective imperialist competitive algorithm (MOICA) model is employed so that the first goal is to maximize the percentage of supplying demands, while the second one is to reduce the violation of allowable capacities of the reservoir during the operation period. In this regard, modelling the operation of the reservoir is conducted based on the current condition for 720 months (i.e., from October 1960 to September 2019). Finally, by introducing the optimization scenario and applying the reservoir hedging policy, the optimization of the operation of the reservoir is done, and the outcomes are compared with the outputs of the reference scenario. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers are yielded after 1,000 iterations. The findings show that the violation of the allowable capacities did not occur in any period in the optimization scenario, while in the reference scenario, the reservoir level reached the dead level in sequent months with more water shortage, which might result in a lack of water supply in such months and serious damages to the system. Due to the application of the hedging principles in the optimization mode, the percentage of supplying the demands in the dry months has extended between 20% and 35% compared to the reference state, which demonstrates a significant reduction in the failure rate in such months compared to the reference mode.

CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis

In this manuscript, a Chaotic Multi Verse Harris Hawks Optimization algorithm based Deep Kernel Machine Learning Classifier (CMVHHO-DKMLC) method is proposed for medical diagnostics. By using CMVHHO, the feature selection (FS) is done for finding ideal feature subset of medical documents. In the proposed method, to obtain the ideal subset of characteristics for best classification, initially chaotic multi-verse optimization (CMV) was implemented to create first positions, after that Harris Hawks optimization (HHO) was utilized to keep positions informed current population at DKMLC-based search space. Then the proposed model is performed on MATLAB and the performance of proposed method is evaluated with assessment metrics. For demonstrating the efficacy of the proposed system on medical diagnosis, this manuscript carries out a series of comparative studies by testing with two medical data sets, i.e. Wisconsin Breast Cancer Database [40] and PIMA Indian Diabetic Dataset [41]. This proposed CMVHHO-DKMLC classifier provides 1.165% and 0.667% higher accuracy value compared with existing classifier model like against the chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and improved gray wolf optimization-based feature selection wrapped kernel extreme learning machine (IGWO-KELM) for breast cancer diagnosis. Similarly the proposed CMVHHO-DKMLC classifier provides 25.641% and 3.55% higher accuracy value compared with existing classifier model like chaotic multi-swarm whale optimizer-boosted support vector machine (CMWOAFS-SVM) and multi-objective firefly and multi-objective imperialist competitive algorithm optimizer-boosted support vector machine (MOFA-MOICA-SVM) for diabetic diagnosis. Simulation outcomes have demonstrated the dominance of proposed technique over other two competitive methods.

Multi-Objective Multi-Skill Resource-Constrained Project Scheduling Problem Under Time Uncertainty

One of the essential features of project management is the novelty of work and uncertainty in the estimation of activities which will, in turn, result in uncertainty in project scheduling. In this study, a multi-objective multi-skill project scheduling problem is examined in terms of fuzzy time with two main objectives including: minimizing the project's makespan and minimizing the total cost of labor allocation. Besides, a Multi-Objective Imperialist Competitive Algorithm (MOICA) is adopted to solve the problem. In order to evaluate the performance of the algorithm, the suggested algorithm is compared with the Non-d ominated Solutions Genetic Algorithm (NSGA-II) based on three indicators. Apart from these, the parameters were optimized using the Taguchi algorithm in order to optimize the performance of the proposed algorithm. The results indicate the good performance of the MOICA.

Solve a New Robust Bi-Objective Model for Designing Blood Supply Chain Network by NSGA II and Imperialist Competitive Algorithm

In this supply chain, blood and blood products are investigated as a product from donor to patient. Specific characteristics of blood supply chain such as perishability and existence of uncertainty in the structure of this chain have caused problems for planning in this regard. However, this point should be considered that the importance of this supply chain cannot be compared to perishable products. Life and death issue of this product is the main difference between blood and other perishable products. Therefore, a comprehensive model was presented in this study to locate blood bank components within a network and to determine the allocation of these components considering blood donation centers, blood testing and processing laboratories, distribution centers or blood banks, and demand centers. Since designing supply chain and locating issues at large dimensions are NP-hard, the suggested models at small sizes were compared using the exact method (GAMS), non-dominated genetic sorting algorithm, and multi-objective imperialist competitive algorithm. The results were compared with GAMS. This shows normal performance of the proposed algorithms and led to using these two algorithms at average and large sizes for research questions. Also, by analyzing sensitivity on important parameters, important managerial findings are suggested for similar conditions.

Multi-objective optimization of energy use and environmental emissions for walnut production using imperialist competitive algorithm

Although the agricultural sector is an important source of bioenergy production, this production can be considered sustainable when energy consumed and environmental emissions are optimal. As such, the assessment of energy flow, environmental emissions of walnut orchards in Alborz province of Iran and their simultaneous optimization by multi-objective imperialist competitive algorithm are the main goals of this investigation. Input-output energy analysis, IMPACT 2002+ method of life cycle assessment, and multi-objective imperialist competitive algorithm are used in the energy-environmental evaluation for optimization in this study. Results ascertain that energy uses of the entire output and input are computed to be 31,015 and 27200 MJ ha−1, respectively and that gasoline with 40% is the dominated consumer of energy. Moreover, energy use efficiency is 0.88, which indicates energy inefficiency in walnut production. Environmental results shows that On-Orchard emissions with a share more than 50% in ecosystem quality, human health, and climate changes and gasoline in resources category are the main hotspots. Multi-objective optimization illustrates that the reduction in total energy is 19316 MJ ha−1 (about 62%) and gasoline with 58% is the most energy saving input among all. On the other hand, the total weighted emission decreases by about 1.47Pt (about 40%). Generally, results reveal that timely maintenance can help orchardist attain close to optimal condition. Furthermore, the application of imperialist competitive algorithm not only can offer optimum pattern of walnut production, but also be extended to the world for different crops.

A dynamic dispatching problem to allocate relief vehicles after a disaster

This article proposes a deterministic model for a post-disaster scenario in an urban emergency medical services system to allocate the emergency vehicles to the patients and transfer them to the hospital. To solve the model, an exact approach called the -constraint method is applied to the problem. Since this problem belongs to the class of NP-hard problems, two metaheuristic algorithms, namely the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective imperialist competitive algorithm (MOICA), are applied to large-scale problems. The performance of the algorithms is evaluated using computational experiments. Finally, the model is applied in a real-life case study for an expected earthquake in Iran and several managerial insights are extracted.

A healthcare logistic network considering stochastic emission of contamination: Bi-objective model and solution algorithm

This paper presents a novel healthcare waste location-routing problem by concentrating on a new perspective in healthcare logistics networks. In this problem, there are healthcare, treatment, and disposal centers. Locations of healthcare centers are known, however, it is required to select appropriate locations for treatment, recycling, and disposal centers. Healthcare wastes are divided into infectious and non-infectious wastes. Since a great volume of healthcare wastes are infectious, the emission of contamination can have obnoxious impacts on the environment. The proposed problem considers a stochastic essence for the emission of contamination which depends on the transferring times. In this respect, transferring times between healthcare and treatment centers have been considered as normal random variables. As transferring time increases, it is more likely for the contamination to spread. Having visited a treatment center, infectious wastes are sterilized and they will no longer be harmful to the environment. This research develops a bi-objective mixed-integer mathematical formulation to tackle this problem. The objectives of this model are minimization of total costs and emission of contamination, simultaneously. Complexity of the proposed problem led the researchers to another contribution. This study also develops a Multi-Objective Water-Flow like Algorithm (MOWFA), which is a meta-heuristic, to solve the problem. This algorithm uses a procedure based on the Analytical Hierarchy Process (AHP) to rank the non-dominated solutions in the archive set. By means of a developed mating operator, the MOWFA utilizes the best ranked solutions of the archive in order to obtain high quality offspring. Two neighborhood operators have been designed for the MOWFA as the local search methods. Extensive computational experiments have been conducted to evaluate the effectiveness of the MOWFA on several test problems compared with other meta-heuristics, namely the Multi-Objective Imperialist Competitive Algorithm (MOICA) and Multi-Objective Simulated Annealing (MOSA). These experiments also include a real healthcare waste logistic network in Iran. The computational experiments demonstrate that our proposed algorithm prevails these algorithms in terms of some well-known performance evaluation measures.

EBST: An Evolutionary Multi-Objective Optimization Based Tool for Discovering Potential Biomarkers in Ovarian Cancer.

Ovarian cancer is the deadliest gynecologic malignancy, mainly due to limitations in early diagnosis. With advances in high-throughput technologies, research interest in identifying novel and customized tumor biomarkers for early detection and diagnosis is rapidly growing. Here we introduce a new tool called EBST to select microRNAs with biomarker potency in ovarian cancer. This tool has pre-processing options and Its core is the use of Modified Multi Objective Imperialist Competitive Algorithm and six objective functions based on the classifier performance/structure evaluation, clustering error and mRMR filter. In this paper, we used the FDR filter in the pre-processing stage and considered five objective functions, four of which relate to the l1-SVM classifier performance and one to the average mRMR ranking. The proposed method has identified 11 microRNAs including hsa-miR-6784-5p, hsa-miR-1228-5p, hsa-miR-8073, hsa-miR-6756-5p, hsa-miR-1307-3p, hsa-miR-4697-5p, hsa-miR-3663-3p, hsa-miR-328-5p, hsa-miR-1228-3p, hsa-miR-6821-5p, hsa-miR-1268a. Data classification by the proposed model showed 100 percent sensitivity, 99.38 percent specificity, 99.69 percent accuracy and 99.39 percent positive predictive value. In comparison with routine state-of-the-art methods, superiority of our method was confirmed. The biological evaluation of selected microRNAs using bioinformatics tools and published articles confirms their role in cancer signaling pathways. The tool and its MATLAB code are freely available at https://github.com/hanif-y.

P-GWO and MOFA: two new algorithms for the MSRCPSP with the deterioration effect and financial constraints (case study of a gas treating company)

This paper presents a bi-objective mathematical formulation for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the deterioration effect and financial constraints. The objectives are to optimize the makespan and cost of project, simultaneously. Due to the high NP-hardness of the proposed model, a Pareto-based Grey Wolf Optimizer (P-GWO) algorithm has been developed to solve the problem. A new procedure based on the Weighted Sum Method (WSM) has been designed for the P-GWO to rank the solutions of population in order to find the alpha, beta, delta, and omega wolves. The P-GWO also uses a new procedure based on the Data Envelopment Analysis (DEA) to keep the most efficient newly found solutions and update the archive of non-dominated solutions. Besides, a Multi-Objective Fibonacci-based Algorithm (MOFA) based on the characteristics of the Fibonacci sequence has been proposed to solve the problem. The MOFA utilizes a novel neighborhood operator to generate as many feasible solutions as required in each iteration. For the MOFA, new procedures for finding the best solution of each iteration, elitism and updating archive of non-dominated solutions have been developed as well. To evaluate the proposed algorithms, a series of numerical experiments have been conducted and the outputs of our proposed methods were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Imperialist Competitive Algorithm (MOICA), and Multi-Objective Fruit-Fly Optimization Algorithm (MOFFOA) in terms of several performance measures. Moreover, a real-life overhaul project in a gas treating company has been studied to demonstrate the practicality of the proposed model. The results of all numerical experiments demonstrate that the P-GWO outperforms other algorithms in terms of most of the metrics. The outputs imply that the MOFA can generate high quality solutions within a reasonable computation time.

A multi-objective methodology for multi-criteria engineering design

Optimization is more and more significant due to its application in the real engineering problems. The recently proposed imperialist competitive algorithm (ICA) is a successful method in mono-objective optimization. Nevertheless, ICA cannot handle simultaneously the conflicting objectives in multi-objective design problem. In addition, the ICA has the drawback of trapping in local optimum solutions when used for high-dimensional or complex multimodal functions. In order to deal with these situations, in this work, an improved ICA, named modified multi-objective imperialist competitive algorithm (MOMICA) is proposed. In MOMICA, an attraction and repulsion (AR) concept is implemented in the assimilation phase to improve the performances of the algorithm to reach the global optimal position. Moreover, in contrast to ICA, the proposed algorithm integrates the sorting non-dominated strategy (SND) to store the Pareto optimal solutions of multiple conflicting functions. Three performance metrics are used to evaluate the performance of the proposed algorithm: (a) convergence to the true Pareto-optimal set, (b) solutions diversity and (c) robustness, characterized by the variance over 10 runs. The results presented in this paper show that the MOMICA algorithm outperforms the other popular techniques in terms of convergence characteristics and global search ability, for both benchmark functions optimization and multi-objective engineering optimization problems.

Time–Cost–Quality Trade-Off in a Broiler Production Project Using Meta-Heuristic Algorithms: A Case Study

The global production of broiler meat was forecasted to be 97.8 MT in 2019. The cost fluctuations create risks in production. In order to have an effective management system, process uncertainty must be taken into account. This approach considers the process as an interval with fuzzy numbers and, for managing the risks, it uses the variable α, a parameter determined by the manager in an interval between 0 and 1. Then two algorithms, namely the multi-objective imperialist competitive algorithm (MOICA) and multi-objective particle swarm optimization (MOPSO), were compared and applied. Since the process of production has many activities and each activity has possible options, the process does not have a unique solution. Therefore, the objective function and its assigned weights in terms of time, cost, and quality can be applied to select the best solution from those obtained. A vast amount of uncertainty can be observed, and effective management necessitates dealing with these uncertainty issues. The MOPSO algorithm showed better performance than the MOICA algorithm in this problem. Based on fuzzy logic and influenced by the uncertainty condition (α = 0), time, cost, and quality in the MOPSO and the MOICA algorithms were 1793.8 h, $260,571.7, and 46.66%, and 1792.5 h, $260,585.7, and 51.19%, respectively.

Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Abstract Nonlinear constrained optimization problem (NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system etc. In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed. First, we review some existing excellent algorithms for solving NOCP; then, the nonlinear constrained optimization problem is transformed into a biobjective optimization problem. Second, in order to improve the diversity of evolution country swarm, and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state-of-the-art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.