A comprehensive survey on gravitational search algorithm

Rashedi, Esmat; Rashedi, Elaheh; Nezamabadi‐pour, Hossein

doi:10.1016/j.swevo.2018.02.018

Cited by 214 publications

(102 citation statements)

References 137 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…GSA [31][32][33][34] deals with consideration of particle vectors as objects and their masses are contributing factors to measure performance. Gravitational force is the main reason of attraction between all objects and it results in global movement of objects with lesser weight towards heavier objects.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…Thus, it is necessary to develop an efficient and robust optimization method. There are various evolutionary optimization techniques such as genetic algorithm (GA) [26][27][28][29], simulated annealing (SA) [30], gravitational search algorithm (GSA) [31][32][33][34], particle swarm optimization (PSO) [35][36][37][38][39][40] etc. for optimization of complex, discontinuous and non-differentiable array factor of the antenna array.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Pattern Synthesis of Thinned and Non-Uniformly Excited Concentric Circular Array Antennas using Hybrid GSA-PSO Technique

Bera¹,

Kundu²,

Pathak³

2019

RADIOENGINEERING

View full text Add to dashboard Cite

Side Lobe Level (SLL) is considered as the most significant array pattern parameter as it helps in reducing surrounding noise and interference. As higher SLL value results in higher wastage of power in undesired direction, transmitters of wireless communication systems face serious problems. In this paper, the optimal design of seven different sets of concentric circular antenna arrays (CCAAs) of isotropic antenna has been represented with the goal of maximum reduction in SLL. Optimal pattern synthesis of the proposed arrays has been executed by optimizing the normalized current distributions of array elements having fixed inter-element spacing. In present work interring spacing has been fixed at 0.5λ. In order to achieve low SLL in the radiation pattern of the optimized array antenna, many conventional optimization methods have been proposed in last few decades for handling complex, non-differentiable, discontinuous and highly nonlinear array factor. To deal with the problems of premature convergence (fall into local optima) feature of gravitational search algorithm (GSA) and particle swarm optimization (PSO) has been merged. In high-dimensional space, gravitational search algorithm hybridized with particle swarm optimization (GSA-PSO) is considered while preserving the fast converging property of them. Numerical results in the present text assume the pattern synthesis of thinned array and non-uniformly excited array for seven different sets of CCAA geometries. An exhaustive simulation results are presented and the radiation pattern performances are analyzed. As compared with conventional optimization techniques like GSA and PSO, hybrid GSA-PSO technique outperforms with the goal of maximum SLL suppression.

show abstract

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Pattern Synthesis of Thinned and Non-Uniformly Excited Concentric Circular Array Antennas using Hybrid GSA-PSO Technique

Bera¹,

Kundu²,

Pathak³

2019

RADIOENGINEERING

View full text Add to dashboard Cite

show abstract

“…(iii) Physics-based algorithms are inspired by physical phenomena. Algorithms like Big Bang-Big Crunch (BB-BC) [31], colliding bodies optimization (CBO) [32], gravitational search algorithm (GSA) [33,34], star graph [35], water wave optimization (WWO) [36], and ray optimization [37] are located in this group. For example, WWO is inspired by refraction and breaking rules of water surface waves.…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Search and Rescue Operations

Shabani

Asgarian

Gharebaghi

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

show abstract

“…It provides fast solution with high-quality results [27]. The initialization of population parameter is configured randomly in the GSA, and the activity approach of reinforcement agents is at first dependent on randomness [28]. If the random guess is not far away from the optimal result, it can be solved in a quick convergence.…”

Section: Introductionmentioning

confidence: 99%

Bidding strategy for generators considering ramp rates in a day-ahead electricity market

Singh¹,

Fozdar²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

In a day-ahead electricity market, competitive bidding strategy plays a vital role for power suppliers to maximize their profit. In this type of market, each power supplier submits a set of hourly production prices and offers capacity for the next period. The market operator, after receiving this data along with forecasted hourly load from the demand side, allocates production output to each unit. Power suppliers face the problem in trading their offers in the market, due to the uncertain behavior of competitive power suppliers and power demand. Therefore, the power supplier requires a suitable bidding strategy for handling uncertainty in the market to maximize their profits. Moreover, the considerations of ramp rates are necessary for the precise representation of practical power system. Thus, in the present work, a modified gravitational search algorithm based on oppositional learning concept is used to solve strategic bidding problem for power suppliers considering six generators with ramp rates, 24-h load data and rivals behavior. The total hourly profits of generators with and without considering ramp rates have been compared.

show abstract

A comprehensive survey on gravitational search algorithm

Cited by 214 publications

References 137 publications

Optimal Pattern Synthesis of Thinned and Non-Uniformly Excited Concentric Circular Array Antennas using Hybrid GSA-PSO Technique

Optimal Pattern Synthesis of Thinned and Non-Uniformly Excited Concentric Circular Array Antennas using Hybrid GSA-PSO Technique

A New Optimization Algorithm Based on Search and Rescue Operations

Bidding strategy for generators considering ramp rates in a day-ahead electricity market

Contact Info

Product

Resources

About