gravitational search algorithm

In light of the No Free Lunch (NFL) theorem, which establishes the inherent limitations of meta-heuristic algorithms in universally efficient problem solving, the ongoing quest for enhanced diversity and efficiency prompts the introduction of novel algorithms each year. This research presents the IWOGSA meta-heuristic algorithm, a pioneering solution tailored for addressing continuous optimization challenges. IWOGSA ingeniously amalgamates principles from both the invasive weed optimization algorithm and the gravitational search algorithm, capitalizing on their synergies. The algorithm's key innovation lies in its dual-pronged sample generation strategy: a subset of samples follows a normal distribution, while others emulate the planetary motion-inspired velocities and accelerations from the gravitational search algorithm. Furthermore, a selective transfer of certain samples from distinct classes contributes to the evolution of succeeding generations. Expanding upon this foundation, a discrete variant of IWOGSA, termed DIWOGSA, emerges to tackle discrete optimization problems. The efficacy of DIWOGSA is demonstrated through its application to the intricate influence maximization problem. DIWOGSA distinguishes itself with an astute population initialization strategy and the integration of a local search operator to expedite convergence. Empirical validation encompasses a rigorous assessment of IWOGSA against established benchmark functions, composite functions, and real-world engineering structural design problems. Remarkably, the IWOGSA algorithm asserts its superiority, eclipsing both contemporary and traditional methods. This ascendancy is statistically affirmed through the utilization of the Friedman test rank, positioning IWOGSA as the premier choice. Also, DIWOGSA algorithm is evaluated by considering different networks for influence maximization problem, and it shows acceptable results in terms of influence and computational time in comparison to conventional algorithms.

Gravitational Search Algorithm (GSA) is a simple and efficient optimization method recently proposed for solving single-objective optimization problems. In this paper, for the first time, the nearest-better neighborhoods are defined in swarm intelligence algorithms and then used in the GSA to solve multi-modal optimization problems. For this purpose, two neighborhoods are defined, called Topological Nearest-Better (TNB) and Distance-based Nearest-Better (DNB), and then these two structures are used separately in the GSA and two different versions of the GSA for multi-modal optimization problems are provided. To investigate the efficiency of the proposed algorithms, an empirical assessment has been performed on several standard multi-modal benchmark functions. The results of these experiments show that the proposed algorithms can achieve good results compared to other multi-modal optimizer algorithms.

Wireless sensor networks include low-weight low cost nodes equipped with limited processing and energy sources. Sensor nodes monitor their surroundings and send sensed events to the sink node through shortest path. One of the major challenges in these networks is to keep nodes connected to each other while the target area is covered effectively. These two parameters are referred as main parameters of quality of service (QoS). In this paper, optimal area coverage is carried out using the Gravitational Search Algorithm. In the proposed algorithm, the agents or masses are equivalent to sensor nodes. The nodes are influenced by distance, Newton gravity law and the laws of motion. A group of nodes can be considered as a cluster. The proposed algorithm is compared with previous methods in terms of network life time, remaining energy and network power. Simulation results show that the proposed method reduces energy consumption by optimizing the number of nodes in the area with maximal coverage, and increases the lifetime and effectiveness of the network.