Application of Shannon Entropy Implementation Into a Novel Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for Optimal Reactive Power Dispatch Problem

Jamal, Raheela; Men, Baohui; Khan, Noor Habib; Raja, Muhammad Asif Zahoor; Yasir, Muhammad

doi:10.1109/access.2020.3046317

Cited by 21 publications

(8 citation statements)

References 74 publications

(70 reference statements)

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“…The reported computational experiments show that this work provides flexibility to the bio–inspired solver to self–organize its inner behaviors. Following this line of research, in [ 45 ], a hybrid algorithm between Shannon entropy and two swarm methods is introduced to improve the yield, memory, velocity, and, consequently, the move update. In [ 46 ], Shannon entropy is integrated into a chaotic genetic algorithm for taking data from solutions generated during the execution.…”

Section: Related Workmentioning

confidence: 99%

Entropy–Based Diversification Approach for Bio–Computing Methods

Olivares

Soto

Crawford

et al. 2022

Entropy

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Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal’s performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

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Section: Related Workmentioning

confidence: 99%

Entropy–Based Diversification Approach for Bio–Computing Methods

Olivares

Soto

Crawford

et al. 2022

Entropy

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“…An enhanced and efficient differential evolution (DE) with new mutation approach is described in [6,7] to solve ORPD problem with HVDC transmission link. A methodology based on entropy evolution approach is proposed in [8] using Gravitational Search technique and fractional particle swarm optimization (PSO) for the solution of ORPD problem. The author in [9] proposes the solution to ORPD considering the system losses and voltage deviation minimization as objectives.…”

Section: A Literature Reviewmentioning

confidence: 99%

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

Salkuti¹

2021

IJACSA

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This paper presents the solution of multi-objective based optimal reactive power dispatch (MO-ORPD) problem by optimizing the system power losses and voltage stability enhancement index (VSEI)/L-index objectives. ORPD problem is considered as an important issue from system security and operational point of view for optimal steady-state operation of power system. Here, single-objective based ORPD problem is solved using Crow Search Algorithm (CSA) and multi-objective based ORPD problem is solved using multi-objective CSA (MO-CSA). The CSA is considered as an efficient and robust algorithm which determines the global optimal solution for solving the non-linear and discontinuous objective functions. Two standard test systems, i.e., IEEE 30 and 57 bus systems are considered to show the effectiveness, suitability and robustness of CSA and MO-CSA for solving the ORPD problem.

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“…In recent years, different methodologies have been developed for the ORPD problem, so a series of algorithms that seek to improve the solution to this problem have emerged. In [14], the ORPD problem is solved via the Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA), which integrates the Particle Swarm Optimization (PSO) algorithm and the Gravitational Search Algorithm (GSA) to improve the solution time and guarantee the convergence of the problem, minimizing the active power losses and voltage variations (voltage deviation). In [15], the authors solve the ORPD problem by considering the uncertainty of solar irradiance and wind speed regarding integrating renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ-Constraint Method and Fuzzy Satisfaction

Villacrés,

Carrión

2023

Energies

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Optimal power dispatch is essential to improve the power system’s safety, stability, and optimal operation. The present research proposes a multi-objective optimization methodology to solve the real and reactive power dispatch problem by minimizing the active power losses and generation costs based on mixed-integer nonlinear programming (MINLP) using the epsilon constraint method and fuzzy satisficing approach. The proposed methodology was tested on the IEEE 30-bus system, in which each objective function was modeled and simulated independently to verify the results with what is obtained via Digsilent Power Factory and then combined, which no longer allows for the simulation of Digsilent Power Factory. One of the main contributions was demonstrating that the proposed methodology is superior to the one available in Digsilent Power Factory, since this program only allows for the analysis of single-objective problems.

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Application of Shannon Entropy Implementation Into a Novel Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for Optimal Reactive Power Dispatch Problem

Cited by 21 publications

References 74 publications

Entropy–Based Diversification Approach for Bio–Computing Methods

Entropy–Based Diversification Approach for Bio–Computing Methods

Solving Reactive Power Scheduling Problem using Multi-objective Crow Search Algorithm

Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ-Constraint Method and Fuzzy Satisfaction

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