Modified Imperialist Competitive Algorithm Research Articles

Modified imperialist competitive algorithm for aircraft landing scheduling problem

Modified imperialist competitive algorithm for aircraft landing scheduling problem

A Software Defined Networking Architecture for DDoS-Attack in the Storage of Multimicrogrids

Multi-microgrid systems can improve the resiliency and reliability of the power system network. Secure communication for multi-microgrid operation is a crucial issue that needs to be investigated. This paper proposes a multi-controller software defined networking (SDN) architecture based on fog servers in multi-microgrids to improve the electricity grid security, monitoring and controlling. The proposed architecture defines the support vector machine (SVM) to detect the distributed denial of service (DDoS) attack in the storage of microgrids. The information of local SDN controllers on fog servers is managed and supervised by the master controller placed in the application plane properly. Based on the results of attack detection, the power scheduling problem is solved and send a command to change the status of tie and sectionalize switches. The optimization application on the cloud server implements the modified imperialist competitive algorithm (MICA) to solve this stochastic mixed-integer nonlinear problem. The effective performance of the proposed approach using an SDN-based architecture is evaluated through applying it on a multi-microgrid based on IEEE 33-bus radial distribution system with three microgrids in simulation results.

Six sigma robust multi-objective design optimization of flat plate collector system under uncertain design parameters

Flat plate collectors (FPCs) can be considered as an important alternative to reduce the high costs of electrical energy used in heating water. Therefore, the development of FPCs with optimal performance has been much focused upon in recent research initiatives. However, due to the existence of uncertainties in material properties, geometry and environmental conditions, it is imperative to incorporate uncertainty analysis into the design optimization to obtain reliable results. In this work, a six sigma robust multi-objective optimization (SSRMO) of the FPC is developed where uncertain design parameters (UDP) are considered. A combined multi-objective modified imperialist competitive algorithm (MOMICA) and Monte Carlo simulation method (MCSM) is developed and used for the SSRMO. The SSRMO considers simultaneously three objectives functions including the FPC efficiency, the FPC cost and their sensitivities to UDP. The obtained results showed that the FPC design obtained by determinist optimization presents 31% sensitivity in terms of efficiency and 27% sensitivity in terms of total cost. However, the SSRMO provides a robust FPC design with the same optimal performances comparable to those obtained by deterministic method but with much lower sensitivity to UDP. Moreover, the robust FPC performances sensitivities were reduced by 60% compared to literature results.

Developing a mathematical model for a multi-door cross-dock scheduling problem with human factors: A modified imperialist competitive algorithm

This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model is developed for this problem, in which the basic truck scheduling problem in a cross-docking system is strongly considered as NP-hardness. Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm, resulted from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used. Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and time criteria. Results show that the hybrid PCICA algorithm performs better than the other algorithms in terms of the solution quality. Computational results indicate when the learning rate increases, its decreasing effect on processing time will growth and the objective function value is improved. Finally, the sensitivity analysis also indicates when the deterioration rate is reduced, its incremental effect is decreased over time.

Design Optimization and Experimental Validation of Low-Cost Flat Plate Collector Under Central Qasssim Climate

In Saudi Arabia, hot water for domestic uses consumes a great portion of home electricity. Thus, solar collectors can be considered as an important alternative to reduce the amount of consumed electricity. Therefore, in recent researches, a great attention was given to develop flat plate collector (FPC) with optimal performance. In this paper, a multi-objective modified imperialist competitive algorithm (MOMICA) was employed for optimizing the performance of a FPC. The optimization results showed a capability to reach higher FPC efficiency with a relatively small collector area and hence lower price. It has also been proved that the change of the insulator depth from 0.02 to 0.05 m has a strong influence on the system’s efficiency.

A Modified Imperialist Competitive Algorithm for Spoofing Attack Detection in Single-Frequency GPS Receivers

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.

A Probabilistic Robust Coordinated Approach to Stabilize Power Oscillations in DFIG-Based Power Systems

The adverse effects resulting from the intermittent nature of wind energy on power generation and stability is well documented in the literature. More specifically, the impact of power oscillations on doubly fed induction generator (DFIG)-based wind turbines can have drastic effects and even lead to large area blackouts, if not addressed properly. This paper deals with the design and implementation of a probabilistic coordinated approach for power systems with synchronous generators (SGs), DFIGs, and facts devices. The parameters of the power system stabilizers (PSSs) of the SG, power oscillation dampers (POD) of the DFIGs and STATCOM controllers were optimally coordinated using a modified Imperialist Competitive Algorithm (MICA) combined with a probabilistic eigenvalue approach. The proposed approach was implemented to a modified 39-bus New England power system. Its effectiveness in guaranteeing power system's dynamic stability was assessed using time domain analysis, eigenvalue mapping, and robustness analysis under various wind conditions. DFIG's realistic modes of operation, including normal, synchronous, and sub-synchronous modes were considered in the performance analysis. The obtained results were further compared to those of deterministic and uncoordinated approaches. A comparison of the MCA algorithm to other optimization approaches such as particle swarm optimization (PSO), genetic algorithm (GA), and imperialist competitive algorithm (ICA) is also carried out. The simulation results and robustness analysis confirmed the superiority of the proposed probabilistic approach over various other approaches.

Solving constrained optimal power flow with renewables using hybrid modified imperialist competitive algorithm and sequential quadratic programming

The objective of this paper was to develop and solve different constrained optimal power flow (OPF) problems for hybrid power systems containing renewable energy sources like wind energy and solar power. OPF seeks to optimize the transmission of electric power by finding a steady state operating point that minimizes gas emission and cost of generated electric power without disturbing network power flow, operating limits and system constraints. The OPF problem was solved using a hybrid modified imperialist competitive algorithm and sequential quadratic programming, (HMICA-SQP). Efficacy of the modified imperialist competitive algorithm (MICA) is further enhanced by the process of hybridization with the sequential quadratic programming (SQP) for rapid local refinement and improved precision of the solution. The potential and effectiveness of the proposed metaheuristic were presented and assessed using three benchmark test systems IEEE 30, IEEE 57 and IEEE 118-bus power systems that incorporates several solar and wind energy sources. Comparison of simulation results with recently published optimization approaches solutions showed that the suggested paradigm is more efficient, robust and provides the lowest cost of generated electric power while keeping low emissions.

Modified imperialist competitive algorithm for environmental constrained energy management of microgrids

Optimal energy management of microgrids with different objectives such as operation cost, air pollution and wider use of renewable energy sources is investigated in many research works. In this paper, the effect of different operation modes of microgrids to solve environmental/economic dispatch problem is evaluated. Different modes of operation of microgrid include islanding operation, grid-connected and unilateral or bilateral exchange of power with main grid. The imperialist competitive algorithm (ICA) is one of the evolutionary algorithms that can model the competing imperialists to absorb colonies and uses it to find optimum solution. In this paper, a new method to improve the ICA algorithm is proposed in which the movement radius of the colonies toward the imperialist is adaptively adjusted in accordance with their position by means of adding direction and velocity parameters to the colonies. Thus, the colonies are attracted and repelled more intelligently by imperialists. The proposed method can create a trade-off between exploitation (i.e. local search) and exploration (i.e. global search) and prevents premature convergence while minimizing trapping in the local minima. Simulation results and comparison with other algorithms confirm high performance of the proposed algorithm.

Multi-period mean–semivariance portfolio optimization based on uncertain measure

In this paper, we discuss a multi-period portfolio selection problem when security returns are given by experts’ estimations. By considering the security returns as uncertain variables, we propose a multi-period mean–semivariance portfolio optimization model with real-world constraints, in which transaction costs, cardinality and bounding constraints are considered. Furthermore, we provide an equivalent deterministic form of mean–semivariance model under the assumption that the security returns are zigzag uncertain variables. After that, a modified imperialist competitive algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

A modified imperialist competitive algorithm for a two-agent single-machine scheduling under periodic maintenance consideration

Scheduling with periodic maintenance has been widely studied. However, multi-agent scheduling with simultaneous considerations of periodic maintenance has hardly been considered until now. In view of this, this research focuses on the problem of scheduling jobs that come from two agents on a single machine under periodic maintenance constraint with the objective of minimising the total completion time of the jobs of the first agent while keeping the maximum tardiness of other agent below or at a fixed level UB. We present some new dominance properties for this strongly NP-hard problem. And next, using these properties, we develop a novel imperialist competitive algorithm for the problem. Various parameters of the proposed algorithm are reviewed by means of Taguchi experimental design. For the evaluation of the proposed ICA, problem data was generated to compare it against a genetic algorithm. The results of computational experiments show the good performance of the proposed algorithm.

A modified imperialist competitive algorithm for a two-agent single-machine scheduling under periodic maintenance consideration

Scheduling with periodic maintenance has been widely studied. However, multi-agent scheduling with simultaneous considerations of periodic maintenance has hardly been considered until now. In view of this, this research focuses on the problem of scheduling jobs that come from two agents on a single machine under periodic maintenance constraint with the objective of minimising the total completion time of the jobs of the first agent while keeping the maximum tardiness of other agent below or at a fixed level UB. We present some new dominance properties for this strongly NP-hard problem. And next, using these properties, we develop a novel imperialist competitive algorithm for the problem. Various parameters of the proposed algorithm are reviewed by means of Taguchi experimental design. For the evaluation of the proposed ICA, problem data was generated to compare it against a genetic algorithm. The results of computational experiments show the good performance of the proposed algorithm.

Multi-Objective Modified Imperialist Competitive Algorithm for Brushless DC Motor Optimization

ABSTRACT 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.

Optimum waste heat recovery from diesel engines: Thermo-economic assessment of nanofluid-based systems using a robust evolutionary approach

Nearly 30% of the input energy to a diesel engine is wasted through the exhaust gas; thus, considerable attention has been directed toward developing efficient heat recovery systems for these engines. Given the demonstrated ability of nanofluids to boost the heat transfer rate of heat exchangers, these heat transfer fluids merit consideration for use in diesel exhaust heat recovery systems. In this study, the effects of employing nanofluids on the optimum design of these systems are investigated. An existing heat diesel engine exhaust heat recovery system is modeled to work with Al2O3/water and a modified imperialist competitive algorithm is employed for the optimization. Seven variables consisting of five heat exchanger geometric characteristics together with nanoparticle volume fraction and coolant mass flow rate are considered as design variables. The heat exchanger cost and charging rate of the storage tank are optimization objectives, while the greenhouse gas savings of the heat recovery system are assessed for measuring the environmental impact of the energy recovery. The results indicate that the proposed approach can overcome the challenge of finding the near-optimal design of this complex system and using nanofluids enhances the performance of the heat recovery heat exchanger.

Security Constrained Unit Commitment (SCUC) formulation and its solving with Modified Imperialist Competitive Algorithm (MICA)

One of the most important optimization problems in operation planning of power systems is unit commitment (UC). Consideration of transmission lines and operation constraints in UC problem leads to a more general problem known as Security Constrained Unit Commitment (SCUC). The SCUC can be formulated as a very large scale mixed-integer problem in practical utility grids. Solving SCUC can take tremendous time due to its huge dimension. In this paper, a formulation of SCUC problem, considering practical constraints and nonlinear characteristics including bus voltage limits, line flow limits, in addition to prevailing constraints such as hourly power demand, system spinning reserves, ramp up and down limits, minimum up and down time (MUT/MDT) limits, and emission limits, is presented. The Modified Imperialistic Competitive Algorithm (MICA), using a priority list (PL) for defining the initial state, is used to solve the mentioned optimization problem. The effectiveness of MICA method to solve the SCUC problem is shown on IEEE 30-bus and 118-bus test systems and is compared with the application of some heuristic methods i.e. genetic algorithm (GA), particle swarm optimization (PSO) and other mathematical approaches i.e. mixed integer programming (MIP).

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.

A novel two-stage evolutionary optimization method for multiyear expansion planning of distribution systems in presence of distributed generation

In this paper, a new approach for multiyear expansion planning of distribution systems (MEPDS) is presented. The proposed MEPDS model optimally specifies the expansion schedule of distribution systems including reinforcement scheme of distribution feeders as well as sizing and location of distributed generations (DGs) during a certain planning horizon. Moreover, it can determine the optimal timing (i.e. year) of each investment/reinforcement. The objective function of the proposed MEPDS model minimizes the total investment, operation and emission costs while satisfying various technical and operational constraints. In order to solve the presented MEPDS model as a complicated multi-dimensional optimization problem, a new two-stage solution approach composed of binary modified imperialist competitive algorithm (BMICA) and Improved Shark Smell Optimization (ISSO), i.e. BMICA+ISSO, is presented. The performance of the suggested MEPDS model and also two-stage solution approach of BMICA+ISSO is verified by applying them on two distribution systems including a classic 34-bus and a real-world 94-bus distribution system as well as a well-known benchmark function. Additionally, the achieved results of BMICA+ISSO are compared with the obtained results of other two-stage solution methods.

Optimal PMU location in power systems using MICA

This study presented a modified imperialist competitive algorithm (MICA) for optimal placement of phasor measurement units (PMUs) in normal and contingency conditions of power systems. The optimal PMU placement problem is used for full network observability with the minimum number of PMUs. For this purpose, PMUs are installed in strategic buses. Efficiency of the proposed method is shown by the simulation results of IEEE 14, 30, 57, and 118-bus test systems. Results of the numerical simulation on IEEE-test systems indicated that the proposed technique provided maximum redundancy measurement and minimum request of PMUs so that the whole system could be topologically observable by installing PMUs on the minimum system buses. To verify the proposed method, the results are compared with those of some recently reported methods. When MICA is used for solving optimal PMU placement (OPP), the number of PMUs would be usually equal to or less than those of the other existing methods. Results indicated that MICA is a very fast and accurate algorithm for OPP solution.

A modified Imperialist Competitive Algorithm for multi-robot stick-carrying application

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 the proposed hybridization and parameter adaptation strategies in practical systems.

A modified imperialist competitive algorithm for scheduling single batch-processing machine with fuzzy due date

This paper considers a fuzzy single batch-processing machine (SBPM) scheduling problem and aims to make it closer to a real-world application through a fuzzy set theory. For this purpose, jobs’ due dates are set to be fuzzy numbers and the membership function of a fuzzy due date assigned to each job represents the degree of satisfaction that a decision maker has with the completion time of that job. The objective is to maximize the total degree of satisfaction or equivalently to minimize the total degree of dissatisfaction over given jobs. Then, the fuzzy mathematical programming model is presented. To solve the model, we propose the imperialist competitive algorithm (ICA) and modify the assimilation policy (i.e., colonies moving) and imperialistic competition in order to overcome its immature convergence and improve its performances. Moreover, due to the significant role of parameters on the quality of random search algorithms, a robust calibration is applied on the parameters using the Taguchi optimization technique. To evaluate the proposed ICA, several random test problems are generated and its performance is compared to the traditional ICA, simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and earliest due date (EDD). The obtained computational results demonstrate the superiority and robustness of the modified ICA.