Hybrid Imperialist Competitive Algorithm Research Articles

A Novel Hybrid Imperialist Competitive Algorithm–Particle Swarm Optimization Metaheuristic Optimization Algorithm for Cost-Effective Energy Management in Multi-Source Residential Microgrids

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.

Enhanced Genetic and Imperialist Competitive Based Algorithms for Reducing Design Feedbacks in the Design Structure Matrix

Nowadays, finding an appropriate interrelated activities sequence in designing complex engineering products, leading to efficient product development, is known as a major concern for managers. Recently, a variety of efforts aim to help the design managers tackle this issue, and one of the most useful management tools among them is the sequencing analysis method in an activity-based design structure matrix (DSM). The sequencing analysis method is the process of reordering the DSM rows and columns to minimize the detrimental effects of feedback loops in the design process. Therefore, in this article, an enhanced imperialist competitive algorithm (ICA), an enhanced genetic algorithm (GA), and a hybrid ICA–GA method are presented to find an activity sequence in DSM with a minimum total feedback length, which is a proper criterion for diminishing the detrimental effects of the feedback loops. To this end, we have improved the ICA and GA methods utilizing two techniques: applying the operators dynamically and tuning the main parameters adaptively. Subsequently, some experiments are designed to evaluate the proposed methods’ performance in terms of the cost value, computational time, and convergence rate on a remote sensing satellite DSM as a case study and eight other DSMs. The performance results demonstrate the superiority of ICA over two other methods. Eventually, an exhaustive comparison with the four well-known methods involving Antares, efficient-simple GA, insertion-based heuristic, and insertion-based simulated annealing illustrates the superiority of the ICA in large-scale problems, yielding the best-known solution in a reasonable computational time.

A hybrid imperialist competitive algorithm for energy-efficient flexible job shop scheduling problem with variable-size sublots

Variable-size batching method has a higher possibility to obtain the most flexible scheduling schemes, which can be better applied to the manufacturing process. However, changing the sizes and number of sublots will not only increase the energy consumption of the manufacturing process, but also increase the search space of the scheduling schemes. A multi-objective optimization problem is formulated considering the makespan and total energy consumption simultaneously. Then, a two-stage multi-objective hybrid algorithm (HICSA) combining imperialist competitive algorithm (ICA) and simulated annealing algorithm (SAA) is proposed to solve this problem and improve the searching efficiency. The ICA and SAA are used to search for the suitable job sequences with optimal machine assignment and the optimal lot splitting schemes in the two stages, respectively. In order to improve the search efficiency, a novel mapping strategy that turns the discrete space into continuous space is applied. Extensive experiments are conducted and the computational results show that HICSA provides promising results for the problem.

A hybrid imperialist competitive algorithm for the outpatient scheduling problem with switching and preparation times

During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency.

A Hybrid Imperialist Competitive Algorithm for the Distributed Unrelated Parallel Machines Scheduling Problem

In this paper, the distributed unrelated parallel machines scheduling problem (DUPMSP) is studied and a hybrid imperialist competitive algorithm (HICA) is proposed to minimize total tardiness. All empires were categorized into three types: the strongest empire, the weakest empire, and other empires; the diversified assimilation was implemented by using different search operator in the different types of empires, and a novel imperialist competition was implemented among all empires except the strongest one. The knowledge-based local search was embedded. Extensive experiments were conducted to compare the HICA with other algorithms from the literature. The computational results demonstrated that new strategies were effective and the HICA is a promising approach to solving the DUPMSP.

Integrated production-inventory-routing problem for multi-perishable products under uncertainty by meta-heuristic algorithms

ABSTRACT The present study aims to introduce an integrated production-inventory-routing problem (PIRP) with a mixed-integer linear programming model, remarking a multi-perishable product, multi-period, and heterogeneous fleets with time windows in a distribution network. The objective of the proposed model is to maximise the total profit, which equals the selling revenue subtract the aggregation of the holding, production, transportation, and utility preference costs. At the production level, a multi-period production system with production capacity constraints is considered, in which the inventory at each stage of production is intended to compute the related holding costs and schedule more appropriate planning. The vehicle routing problem is tackled at the distribution level regarding vehicles with various capacities in a multi-period condition. Consequently, a fuzzy chance-constrained programming model is used to deal with fuzzy parameters. Furthermore, two evolutionary algorithms, namely a hybrid imperialist competitive algorithm (HICA) and self-adaptive differential evolution (SADE), are proposed to solve the given problem. Subsequently, several numerical examples with managerial insights are solved to evaluate the performances of the proposed algorithms and show their effectiveness and efficiency. Computational results demonstrate the superiority of the proposed algorithms for this problem. Finally, the applicability of the proposed algorithms is investigated by a real-case study.

Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

Hybrid Imperialist Competitive Evolutionary Algorithm for Solving Biobjective Portfolio Problem

Hybrid Imperialist Competitive and Grey Wolf Algorithm to Solve Multiobjective Optimal Power Flow with Wind and Solar Units

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.

Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm

Abstract Global hydropower growth continues to increase with a relative higher rate of total installed capacity in just last 10 years. This expansion means that it is increasingly important to make a further research with respect to various hydropower stations. As the core of hydropower stations, hydraulic turbine regulating system (HTRS) attracts many attentions. In essence, HTRS is a complex nonlinear system that governs the frequency and the electrical power output of hydroelectric unit. The design of the control laws for the HTRS is an important and difficult task. In this study, a hybrid imperialist competitive algorithm (HICA) for the dynamic model of HTRS system is proposed and applied to estimate the parameters of fuzzy sliding mode controller (FSMC). In the proposed approach, sliding mode controller (SMC) is regarded as a robust control technique to the external uncertain load disturbances and fuzzy logic rule provides a better proportional gain and reduces the inherent chattering effect of the SMC controller. The HICA is developed to search optimal values of the control law and the membership functions of fuzzy logic rules. Simulations are carried out to verify the effectiveness of proposed approach, where the results show that compared with parallel PID controller and conventional SMC controller, the designed FSMC controller performs much better in terms of system performance and chattering reduction. Also, the results certify the superiority of the HICA algorithm in estimating the parameters for the proposed controller of HTRS in comparison to other classical evolutionary algorithms, where HICA reduces the value of the objective function by 3.28%, 5.33% and 9.69% compared with ICA, BSA, and PSO under unload condition, and HICA reduces the value of the objective function by 3.69%, 4.01% and 10.70% compared with ICA, BSA, and PSO under load condition.

A multiobjective hybrid imperialist competitive algorithm for multirobot cooperative assembly line balancing problems with energy awareness

The environmental and economic pressures caused by energy consumption arouse energy-saving consciousness of the manufacturing industry. In recent years, robots have been extensively used in assembly systems as called robotic assembly lines where the energy consumption is a major expense, particularly in the workstation with the multirobot cooperative assembly of multirobot. To deal with this problem, the paper presents a novel mathematical model with three objectives of minimizing the cycle time, the sum of energy consumption, and the total cost of robots of assembly lines. Due to the nondeterministic polynomial time nature of the considered problem, a multiobjective hybrid imperialist competitive algorithm with nondominated sorting strategy is developed, which uses a representation technique of three-level coding, i.e. the station level, the task level, and the robot level and proposes an original concept of workstation decision assignment matrix to identify the performed tasks by the same type of robots in a workstation. Furthermore, a late-acceptance hill-climbing algorithm is combined into the algorithm to improve the performance of the proposed algorithm. Finally, testing cases are designed to measure the performance of the proposed method by comparing with two other high-performing multiobjective methods. The computational and statistical results show that the proposed multiobjective hybrid imperialist competitive algorithm is conducive to improve the line efficiency, to reduce the sum of energy consumption, and to cut the total cost of robots in an assembly line effectively.

Service composition and optimal selection in cloud manufacturing: landscape analysis and optimization by a hybrid imperialist competitive and local search algorithm

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.

A visible-range computer-vision system for automated, non-intrusive assessment of the pH value in Thomson oranges

Fruit may be classified for the purposes of usage, packaging and marketing based on the pH (potential of hydrogen) value – a numeric scale used to specify the acidity or basicity of an aqueous solution measured in units of moles per liter of hydrogen ions. In this study, a new approach for the automated and non-intrusive estimation of the pH value of the Thomson navel orange (CRC 969, Citrus sinensis) fruit is presented based on visible-range image processing, image feature extraction and with the use of hybrid imperialist competitive algorithm (ICA)-artificial neural network (ANN) regression. Image features studied include length, width, area, eccentricity, perimeter, blue-value, green-value, red-value, width, contrast, texture, roughness and several ratios thereof. Principal component analysis (PCA) is applied to reduce the number of dimensions without loss of important information and a cubic polynomial function of the mean square error (MSE) versus several factors is computed using the response surface methodology (RSM) approach. Results for pH prediction are given and compared with true measured pH values over the entire 100 Thomson orange dataset, including estimated pH scatter regression plots and estimated pH boxplots. Cross validation is performed over 1000 repeated random trial experiments with uniform random train- and test-sample sets (80% training and 20% disjoint test samples). In addition, we provide numerical results based on the levels achieved by response surface methodology (RSM) evaluated over various error coefficients: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination (R2), the root mean square error (RMSE), and MSE, resulting in R2 = 0.843 ± 0.043, MSE = 0.046 ± 0.022, MAE = 0.166 ± 0.039, SSE = 0.915 ± 0.425, and RMSE = 0.214 ± 0.146, over the test set. The results demonstrate that such an automated pH-based sorting system with machine vision using the hybrid ICA-ANN algorithm can accurately compute the pH value of Thomson oranges without any contact with the fruit, and which has clear potential applications in the food industry.

Multiobjective Program and Hybrid Imperialist Competitive Algorithm for the Mixed-Model Two-Sided Assembly Lines Subject to Multiple Constraints

A mixed-model two-sided assembly line is a manufacturing system designed for the production of large-sized products. In order to describe the actual condition, this paper presents a novel multiobjective programming model for balancing a mixed-model two-sided assembly line subject to multiple constraints, in which, additional constraints including zoning, synchronous, and positional constraints are considered besides the traditional constraints, e.g., the precedence constraint. Two objectives are simultaneously to be optimized, one is to minimize the combination of the weighted line efficiency and the weighted smoothness index, and the other is to minimize the weighted total relevant costs per unit of a product. A novel multiobjective hybrid imperialist competitive algorithm (MOHICA) is proposed to solve this problem. In the presented MOHICA, the sigma method is employed to quantify every individual, a novel merging method is introduced to reserve better individuals into the evolutionary population, and late acceptance hill-climbing (LAHC) algorithm is presented as a local search algorithm to achieve accurate balance between intensification and diversification. The experimental results on the selected benchmark instances and a practical case show that the proposed multiobjective algorithm outperforms nondominated sorting genetic algorithm (NSGA)-II, multiobjective improved teaching-learning-based optimization, and NSGA-III existing in the literature.

Simultaneous optimisation of seaside operations in container terminals: a case study of the Iranian Rajaee port

In marine transport, there is a growing need for optimisation surveys, which are aimed at increasing the efficiency of the whole process. In this study, the berth allocation problem, the quay crane assignment problem and the quay crane scheduling problem are simultaneously formulated into an integrated mathematical model. Afterwards, the proposed mixed integer mathematical model is coded via GAMS IDE/CPLEX software. The exact solver appears to need a huge amount of time to find the optimum solution, even for small and medium-sized problems. Hence, artificial intelligence, which is embedded in both imperialist competitive and genetic algorithms, is employed through a highly modified meta-heuristic method. This method is called a 'hybrid imperialist competitive and genetic algorithm' (HICGA) and is designed to deal with the complexity of such problems. To check the validity of the proposed model and the performance of the designed HICGA method, historical data for 24 weeks from the Iranian Rajaee port were provided.

A hybrid imperialist competitive algorithm for solving economic lot and delivery scheduling problem in a four-stage supply chain

In this article, we study the economic lot and delivery scheduling problem for a four-stage supply chain that includes suppliers, fabricators, assemblers, and retailers. All of the parameters such as demand rate are deterministic and production setup times are sequence-dependent. The common cycle time and integer multipliers policies are adapted as replenishment policies for synchronization throughout the supply chain. A new mixed integer nonlinear programming model is developed for both policies, the objective of which is the minimization of inventory, transportation, and production setup costs. We propose a new hybrid algorithm including a modified imperialist competitive algorithm which is purposed to the assimilation policy of imperialist competitive algorithm and teaching learning–based optimization which is added to improve local search. A hybrid modified imperialist competitive algorithm and teaching learning–based optimization is applied to find a near-optimum solution of mixed integer nonlinear programming in large-sized problems. The results denoted that our proposed algorithm can solve different size of problem in reasonable time. This procedure showed its efficiency in medium- and large-sized problems as compared to imperialist competitive algorithm, modified imperialist competitive algorithm, and other methods reported in the literature.

Two‐level procedure based on HICAGA to determine optimal number, locations and operating points of SVCs in Isfahan–Khuzestan power system to maximise loadability and minimise losses, TVD and SVC installation cost

Increasing maximum power system loadability while maintaining bus voltages and line loadings constraints by flexible AC transmission system (FACTS) devices is one of the main issues in power system studies. One of the shunt FACTS devices widely used in power system is static var compensator (SVC). Its performance on power system is highly dependent on its number, location and operating point. This study presents a two-level procedure to find optimal number, locations, and operating points of SVCs in a power system. At the first level, the goal is to maximise loadability of the power system. At this level, by using a method based on iterative process, applying SVCs to the power system, and increasing load factor, the optimisation algorithm seeks to find locations and operating points of SVCs that fulfil bus voltages and line loading constraints. This process continues until the increase in the number of SVCs does not lead to an increase in loadability. At the second level and for a certain number of SVCs, power system losses, total voltage deviations (TVD), and SVC installation cost are minimised at specific loadability. In this study, hybrid imperialist competitive algorithm and genetic algorithm (HICAGA) have been used for optimisation.

An interactive possibilistic programming approach for a multi-objective hub location problem: Economic and environmental design

This paper presents a new multi-objective mathematical model for a multi-modal hub location problem under a possibilistic-stochastic uncertainty. The presented model aims to minimize the total transportation and traffic noise pollution costs. Furthermore, it aims to minimize the maximum transportation time between origin-destination nodes to ensure a high probability of meeting the service guarantee. In order to cope with the uncertainties and the multi-objective model, we propose a two-phase approach, including fuzzy interactive multi-objective programming approach and an efficient method based on the Me measure. Due to the NP-hardness of the presented model, two meta-heuristic algorithms, namely hybrid differential evolution and hybrid imperialist competitive algorithm, are developed. Furthermore, a number of sensitivity analyses are provided to demonstrate the effectiveness of the presented model. Finally, the foregoing meta-heuristics are compared together through different comparison metrics.

Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm

After the completion of a job on a machine, it needs to be transported to the next machine, actually taking some time. However, the transportation times are commonly neglected in the literature. This paper incorporates the transportation times between the machines into the flexible job-shop scheduling problem. We mathematically formulate the problem by two mixed integer linear programming models. Since the problem is NP-hard, we propose an adaptation of the imperialist competitive algorithm hybridized by a simulated annealing-based local search to solve the problem. Various operators and parameters of the algorithm are calibrated using the Taguchi method. The presented algorithm is assessed by comparing it against two other competitive algorithms in the literature. The computational results show that this algorithm has an outstanding performance in solving the problem.

Optimizing an inventory model with fuzzy demand, backordering, and discount using a hybrid imperialist competitive algorithm

Vendor-managed inventory (VMI) is one of the popular policies in retailer-supplier partnerships to reduce total inventory costs in supply chain management (SCM). As there is a lack of studies on simultaneous consideration of backordering and discount in the VMI literature, this paper develops a constrained VMI model with fuzzy demand for a single-vendor multi-retailer supply chain, in which the centroid defuzzification method defuzzifies trapezoidal fuzzy numbers of the demand. While there are two constraints, namely warehouse space and number of orders, the aim of this paper is to find a near-optimal solution including the order size, the replenishment rate of the retailers, the maximum backorder quantities of retailers' item, and the suitable price of the item to minimize the total inventory cost. As the developed problem is shown to be NP-complete, a hybrid imperialist competitive algorithm (HICA) is used to find a near-optimum solution. As there is no benchmark available in the literature and that HICA is a population-based meta-heuristic, a single-solution-based meta-heuristic is employed to validate the results obtained. The technique for order preference by similarity to ideal solution (TOPSIS) determines the better algorithm in solving the problem in terms of six criteria. Moreover, the effects of some cost parameters on the solution obtained are investigated to provide managerial insights. Finally, conclusion and future research are presented.

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

This paper addresses preemption in just-in-time (JIT) single–machine-scheduling problem with unequal release times and allowable unforced machine idle time as realistic assumptions occur in the manufacturing environments aiming to minimise the total weighted earliness and tardiness costs. Delay in production systems is a vital item to be focussed to counteract lost sale and back order. Thus, JIT concept is targeted including the elements required such as machine preemption, machine idle time and unequal release times. We proposed a new mathematical model and as the problem is proven to be NP-hard, three meta-heuristic approaches namely hybrid particle swarm optimisation (HPSO), genetic algorithm and imperialist competitive algorithm are employed to solve the problem in larger sizes. In HPSO, cloud theory-based simulated annealing is employed with a certain probability to avoid being trapped in a local optimum. Taguchi method is applied to calibrate the parameters of the proposed algorithms. A number of numerical examples are solved to demonstrate the effectiveness of the proposed approach. The performance of the proposed algorithms is evaluated in terms of relative percent deviation and computational time where the computational results clarify better performance of HPSO than other algorithms in quality of solutions and computational time.