A T-cell algorithm for solving dynamic optimization problems

Aragón, Victoria S.; Esquivel, Susana Cecilia; Coello, Carlos A. Coello

doi:10.1016/j.ins.2011.04.028

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…The T-Cell algorithm is inspired by the mediated immune cells in the human body. These cells are called lymphocytes and develop in the Thymus as group of white blood cells [14]. They play a central role in cell-mediated immunity.…”

Section: Proposed Algorithm For the Real-time Optimizationmentioning

confidence: 99%

“…Each type of effector cell helps to eliminate the antigen presented in the activation process [13]. Therefore, during an antibody intrusion, the T-Cells proliferate by generating other clones of themselves; then, each clone differentiates by acquiring new proprieties to destroy the intrusion [14]. The authors in [15] tested the performance of T-Cell algorithm compared to other optimization heuristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm

et al. 2019

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The real-time operation of the energy management system (RT-EMS) is one of the vital functions of Microgrids (MG). In this context, the reliability and smooth operation should be maintained in real time regardless of load and generation variations and without losing the optimum operation cost. This paper presents a design and implementation of a RT-EMS based on Multiagent system (MAS) and the fast converging T-Cell algorithm to minimize the MG operational cost and maximize the real-time response in grid-connected MG. The RT-EMS has the main function to ensure the energy dispatch between the distributed generation (DG) units that consist in this work on a wind generator, solar energy, energy storage units, controllable loads and the main grid. A modular multi-agent platform is proposed to implement the RT-EMS. The MAS has features such as peer-to-peer communication capability, a fault-tolerance structure, and high flexibility, which make it convenient for MG context. Each component of the MG has its own managing agent. While, the MG optimizer (MGO) is the agent responsible for running the optimization and ensuring the seamless operation of the MG in real time, the MG supervisor (MGS) is the agent that intercepts sudden high load variations and computes the new optimum operating point. In addition, the proposed RT-EMS develops an integration of the MAS platform with the Data Distribution Service (DDS) as a middleware to communicate with the physical units. In this work, the proposed algorithm minimizes the cost function of the MG as well as maximizes the use of renewable energy generation; Then, it assigns the power reference to each DG of the MG. The total time delay of the optimization and the communication between the EMS components were reduced. To verify the performance of our proposed system, an experimental validation in a MG testbed were conducted. Results show the reliability and the effectiveness of the proposed multiagent based RT-EMS. Various scenarios were tested such as normal operation as well as sudden load variation. The optimum values were obtained faster in terms of computation time as compared to existing techniques. The latency from the proposed system was 43% faster than other heuristic or deterministic methods in the literature. This significant improvement makes this proposed system more competitive for RT applications.

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Section: Proposed Algorithm For the Real-time Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm

et al. 2019

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“…Immune primary and secondary responses are studied to solve time dependent optimization problems [10]. The dynamic T-cell model (DTC), which takes inspirations from immune T-cells, was proposed to handle the DOPs [11]. Then, a modified version named Dynamic Constrained T-Cell (DCTC) was proposed to handle DOPs with constraints [12].…”

Section: Ais For Dopsmentioning

confidence: 99%

Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

Zhang¹,

Zhang²,

Zhang³

et al. 2018

IJPE

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Artificial immune systems (AIS) have been widely applied in optimization under static situations. Due to their dynamism, particular challenges are posed when handling dynamic optimization problems (DOPs). The designed algorithms must overcome these challenges to accomplish efficient results. In the paper, a new AIS based algorithm denoted as IAMAIS is proposed. In this algorithm, innate and adaptive responses in the immune system are elaborated on. The innate response is introduced to maintain the diversity of the population and implement global search, while the adaptive immune response is developed to locally locate the optima. Moreover, a memory mechanism is presented to reserve the found optima and further track the optima when environmental change happens. The experiments were applied on the most well-known benchmark, the Moving Peak Benchmark. Simulation results show that IAMAIS is competitive for Dops.

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“…To evaluate the performance of the proposed algorithm, the same scenarios are formed and tested to compare with the known AIS-based algorithms, including AIIA [10], CLONALG [12], Sais [8], BCA [11], Opt-aiNet [4], and DTC [13]. The mean offline errors on STCG and MPB as comparison results can be found in Table IV. …”

Section: Comparison With the Ais-based Algorithmsmentioning

confidence: 99%

“…Simple Test-Case Generator(STCG) and Moving Peaks Benchmark (MPB) are used to illustrate the performance.In[13], a dynamic T-cell model (DTC) inspired by the processes of T-cells within the immune system wasproposed to solve the DOPs. The experimental results on benchmark functions are very competitive.…”

mentioning

confidence: 99%

A quasi-gradient and cluster-based artificial immune system for dynamic optimization

Zhang

Yen

2013

2013 IEEE Congress on Evolutionary Computation

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This paper presents an artificial immune system for solving dynamic optimization problems. For effectively solving optimization problems in dynamic environments, search population should be able to fast converge in each environment and redistribute when change occurs and subsequently track the change. This paper presents three modifications made to the basic artificial immune system to meet these requirements. The first one is the modified gradient-based mechanism called quasi-gradient which is beneficial to speed up the convergence of population. The second one is clustering strategy which is adopted to effectively distribute the population. Memory mechanism is the last one which takes advantage of the historical information from the last scenario preparing for the upcoming new environment.Applications on classic simple test-case generator and moving peak problem validate the performance of the proposed algorithm.

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A T-cell algorithm for solving dynamic optimization problems

Cited by 17 publications

References 26 publications

An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm

An Optimal Energy Management System for Real-Time Operation of Multiagent-Based Microgrids Using a T-Cell Algorithm

Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

A quasi-gradient and cluster-based artificial immune system for dynamic optimization

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