Gravitational Search Algorithm (GSA) is an optimization method inspired by the theory of Newtonian gravity in physics. Till now, many variants of GSA have been introduced, most of them are motivated by gravity-related theories such as relativity and astronomy. On the one hand, to solve different kinds of optimization problems, modified versions of GSA have been presented such as continuous (real), binary, discrete, multimodal, constraint, single-objective, and multi-objective GSA. On the other hand, to tackle the difficulties in real-world problems, the efficiency of GSA has been improved using specialized operators, hybridization, local search, and designing the self-adaptive algorithms. Researchers have utilized GSA to solve various engineering optimization problems in diverse fields of applications ranging from electrical engineering to bioinformatics. Here, we discussed a comprehensive investigation of GSA and a brief review of GSA developments in solving different engineering problems to build up a global picture and to open the mind to explore possible applications. We also made a number of suggestions that can be undertaken to help move the area forward.

A comprehensive survey on gravitational search algorithm

How to improve the global search ability without significantly impairing the convergence speed is still a big challenge for most of the meta-heuristic optimization algorithms. In this paper, a concept for the optimization of continuous nonlinear functions applying crisscross optimization algorithm is introduced. The crisscross optimization algorithm is a new search algorithm inspired by Confucian doctrine of gold mean and the crossover operation in genetic algorithm, which has distinct advantages in solution accuracy as well as convergence rate compared to other complex optimization algorithms. The procedures and related concepts of the proposed algorithm are presented. On this basis, we discuss the behavior of the main search operators such as horizontal crossover and vertical crossover. It is just because of the perfect combination of both, leading to a magical effect on improving the convergence speed and solution accuracy when addressing complex optimization problems. Twelve benchmark functions, including unimodal, multimodal, shifted and rotated functions, are used to test the feasibility and efficiency of the proposed algorithm. The experimental results show that the crisscross optimization algorithm has an excellent performance on most of the test functions, compared to other heuristic algorithms. At the end, the crisscross optimization algorithm is successfully applied to the optimization of a large-scale economic dispatch problem in electric power system. It is concluded that the crisscross optimization algorithm is not only robust in solving continuous nonlinear functions, but also suitable for addressing the complex real-world engineering optimization problems.

Crisscross optimization algorithm and its application

The gravitational search algorithm (GSA) is a meta-heuristic algorithm based on the theory of Newtonian gravity. This algorithm uses the gravitational forces among individuals to move their positions in order to find a solution to optimization problems. Many studies indicate that the GSA is an effective algorithm, but in some cases, it still suffers from low search performance and premature convergence. To alleviate these issues of the GSA, an aggregative learning GSA called the ALGSA is proposed with a self-adaptive gravitational constant in which each individual possesses its own gravitational constant to improve the search performance. The proposed algorithm is compared with some existing variants of the GSA on the IEEE CEC2017 benchmark test functions to validate its search performance. Moreover, the ALGSA is also tested on neural network optimization to further verify its effectiveness. Finally, the time complexity of the ALGSA is analyzed to clarify its search performance.

An aggregative learning gravitational search algorithm with self-adaptive gravitational constants

A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm

In recent years, various heuristic algorithms based on natural phenomena and swarm behaviors were introduced to solve innumerable optimization problems. These optimization algorithms show better performance than conventional algorithms. Recently, the gravitational search algorithm (GSA) is proposed for optimization which is based on Newton's law of universal gravitation and laws of motion. Within a few years, GSA became popular among the research community and has been applied to various fields such as electrical science, power systems, computer science, civil and mechanical engineering, etc. This chapter shows the importance of GSA, its hybridization, and applications in solving clustering and classification problems. In clustering, GSA is hybridized with other optimization algorithms to overcome the drawbacks such as curse of dimensionality, trapping in local optima, and limited search space of conventional data clustering algorithms. GSA is also applied to classification problems for pattern recognition, feature extraction, and increasing classification accuracy.

Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification

This study introduces a variant of Gravitational Search Algorithm (GSA) for discrete optimization problems, namely, Fast Discrete Gravitational Search Algorithm (FDGSA). The main difference between the proposed FDGSA and the existing Binary Gravitational Search Algorithm (BGSA) is that an agent's position is updated based on its direction and velocity. Both the direction and velocity determine the candidates of integer values for the position update of an agent and then the selection is done randomly. Unimodal test functions, such as De Jong's function, Scwefel's function and Rosenbrock's valley are used to evaluate the performance of the proposed FDGSA. Comparison with BGSA is done to benchmark the proposed FDGSA in terms of speed of convergence and quality of solution. The experimental result shows that the proposed FDGSA converges faster compared to the BGSA.

A Fast Discrete Gravitational Search Algorithm

An improved two-step supervised learning algorithm of Artificial Neural Networks (ANN) for imbalanced dataset problems is proposed in this paper. Particle swarm optimization (PSO) is utilized as ANN learning mechanism for first step and second step. The fitness function for both steps is Geometric Mean (G-Mean). Firstly, the best weights on network are determined with a decision threshold is set to 0.5. After the first step learning is accomplished, the best weights will be used for second step learning. The best weights with the best value of decision threshold are obtained and can be used to predict an imbalanced dataset. Haberman's Survival datasets, which is available in UCI Machine Learning Repository, is chosen as a case study. G-Mean is chosen as the evaluation method to define the classifier's performance for a case study. Consequently, the proposed approach is able to overcome imbalanced dataset problems with better G-Mean value compared to the previously proposed ANN.

Hasrul Che Shamsudin

Papers

A Fast Discrete Gravitational Search Algorithm

An Improved Two-Step Supervised Learning Artificial Neural Network for Imbalanced Dataset Problems