This paper introduces an Improved Artificial Bee Colony algorithm for modeling and managing Micro Grid (MG) connected system. IABC differs from ABC because of its inclusion of Gravitational search algorithm (GSA) in the scout bee phase. Hence, the scout bee phase is substantially improved as the gravitational constant of GSA increases searching accuracy. As already ABC works with memory, IABC t...

Network reconfiguration is an effective approach to reduce the power losses in distribution system. Recent studies have shown that the reconfiguration problem considering load profiles can give a significant improvement on the distribution network performance. This work proposes a novel method to determine the optimal daily configuration based on variable photovoltaic (PV) generation output and...

Abstract The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. A coupled model based on the Gravitational Search Algorithm (GSA) Seasonal Autoregressive Integrated Moving Average (SARIMA) proposed to address non-linear seasonal features data. GSA has a significant local optimisation capability, while SARIMA allows real-time ...

Job Shop scheduling problem has significant importance in many researching fields such as production management and programming and also combined optimizing. Job Shop scheduling problem includes two sub-problems: machine assignment and sequence operation performing. In this paper combination of particle swarm optimization algorithm (PSO) and gravitational search algorithm (GSA) have been presen...

Cities are growing and encountering many changes over time due to population growth and migration. Identification and detection of these changes play important roles in urban management and sustainable development. Urban growth models are divided into two main categories: first cellular models which are further divided into experimental, dynamic, and integrated models and second vector models. ...

Gravitational Search Algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problems, the process of grouping data into clusters such that the data in each c...

Nature-inspired metaheuristics for optimization have proven successful, due to their fine balance between exploration and exploitation of a search space. This balance can be further refined by hybridization. In this paper, we conduct experiments with some of the most promising nature-inspired metaheuristics, for assessing their performance when using them to replace backpropagation as a learnin...

Complex optimization problems that cannot be solved using exhaustive search require efficient search metaheuristics to find optimal solutions. In practice, metaheuristics suffer from various types of search bias, the understanding of which is of crucial importance, as it directly pertinent to the problem of making the best possible selection of solvers. Two metrics are introduced in this study:...

This paper attempts to bring forward various newly emerged natural computing techniques to a common platform. Six such techniques are compared among each other which have been used to solve a w ell known classical problem, the travelling salesman problem. The techniques discussed in this paper are Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bacterial Foraging Optimization ...

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection optimum process parameter levels in any process, numerous metaheuristic algorithms have been proposed so far. However, many are either computationally too expensive or become trapped pit local optima. counter these challenges, this paper, hybrid called PSO-GSA emp...