GEPSO: A new generalized particle swarm optimization algorithm

Sedighizadeh, Davoud; Masehian, Ellips; Sedighizadeh, Mostafa; Akbaripour, Hossein

doi:10.1016/j.matcom.2020.08.013

Cited by 80 publications

(37 citation statements)

References 26 publications

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“…The ABO applying the lean metaheuristic design concept was designed to be fast in obtaining results, avoid stagnation, use few parameters, and be efficient and effective; hence, it is the choice for this comparative study. It was actually designed to complement the existing algorithms such as the Genetic Algorithm [ 52 ], Simulated Annealing [ 53 ], Ant Colony Optimization [ 54 ], and Particle Swarm Optimizations [ 55 ]. Using these vocalizations, the African buffalos organize themselves in their navigation through the African forests in search of lush green pastures to satisfy their huge appetite [ 35 ].…”

Section: The Comparative Algorithmsmentioning

confidence: 99%

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Odili¹,

Ahmad

Zarina

2021

Computational Intelligence and Neuroscience

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This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

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Section: The Comparative Algorithmsmentioning

confidence: 99%

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Odili¹,

Ahmad

Zarina

2021

Computational Intelligence and Neuroscience

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“…Currently, numerous papers propose some nonconventional and hybrid algorithms, which deal with stochastic optimization problem. 20,21 These algorithms integrate two or more modeling methods such that they can use the advantages of the individual ones simultaneously. This paper uses the hybrid algorithm based on IGDT-MOCMA-ES for optimal operation of SDN by considering DFR as well as DR.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach based on IGDT‐MOCMA‐ES method for optimal operation of smart distribution network under severe uncertainties

2021

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Uncertainties involved in renewable generation and electrical demand pose significant technical challenges with the concomitant financial consequences in smart distribution networks (SDNs), particularly in the current electricity market, which is restructured and features smart grids. The present paper introduces a decision-making tool based on a risk-averse strategy to help with the smart distribution network operator (SDNO) in day-ahead operational practices, including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The tool is meant to reduce electricity prices presented to the electrical consumers and to optimize financial transactions with the energy market, distributed generation (DG) reliability, electricity storage system (ESS) dispatch, and planning interruptible electrical demands to secure specified revenue targets for SDNO with the risk-averse strategy. A bi-level stochastic optimization problem based on Information gap decision theory (IGDT) is considered to keep the SDNO from risks inherent in the information gap present between the predicted and actual uncertainty variables. The bilevel stochastic optimization problem is reorganized into a single-level problem obtained by Karush-Kuhn-Tucker method. As uncertainty variables compete to expand their enveloped-bounds, multi-objective covariance matrix adaptationevolution strategy (MOCMA-ES) is employed to address the multifaceted IGDTbased stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the suggested model are appraised on an IEEE 33-bus SDN. Simulation results show that optimal UC with both DFR and demand response program increases the total revenue by 8.1% compared to optimal operation without them.

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“…Ratnaweera et al proposed an asynchronous time-varying acceleration factor [28]. At the same time, researchers have analyzed the convergence of the algorithm and proposed methods to improve the convergence [29][30] [31]. A large number of engineering practices show that compared with other optimization algorithms, particle swarm optimization needs fewer parameters to adjust, and its structure is simple, so it is easier to implement in engineering.…”

Section: A Backgroundmentioning

confidence: 99%

Load Distribution Optimization of Multi-Source District Heating System Based on Fuzzy Analytic Hierarchy Process

et al. 2020

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GEPSO: A new generalized particle swarm optimization algorithm

Cited by 80 publications

References 26 publications

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

A hybrid approach based on IGDT‐MOCMA‐ES method for optimal operation of smart distribution network under severe uncertainties

Load Distribution Optimization of Multi-Source District Heating System Based on Fuzzy Analytic Hierarchy Process

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