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

A gravitational search algorithm &#x0028 GSA &#x0029 uses gravitational force among individuals to evolve population. Though GSA is an effective population-based algorithm, it exhibits low search performance and premature convergence. To ameliorate these issues, this work proposes a multi-layered GSA called MLGSA. Inspired by the two-layered structure of GSA, four layers consisting of population, iteration-best, personal-best and global-best layers are constructed. Hierarchical interactions among four layers are dynamically implemented in different search stages to greatly improve both exploration and exploitation abilities of population. Performance comparison between MLGSA and nine existing GSA variants on twenty-nine CEC2017 test functions with low, medium and high dimensions demonstrates that MLGSA is the most competitive one. It is also compared with four particle swarm optimization variants to verify its excellent performance. Moreover, the analysis of hierarchical interactions is discussed to illustrate the influence of a complete hierarchy on its performance. The relationship between its population diversity and fitness diversity is analyzed to clarify its search performance. Its computational complexity is given to show its efficiency. Finally, it is applied to twenty-two CEC2011 real-world optimization problems to show its practicality.

A multi-layered gravitational search algorithm for function optimization and real-world problems

This paper proposes a new approach to the stable design of fuzzy logic control systems that deal with a general class of chaotic processes. The stable design is carried out on the basis of a stability analysis theorem, which employs Lyapunov's direct method and the separate stability analysis of each rule in the fuzzy logic controller (FLC). The stability analysis theorem offers sufficient conditions for the stability of a general class of chaotic processes controlled by Takagi---Sugeno---Kang FLCs. The approach suggested in this paper is advantageous because inserting a new rule requires the fulfillment of only one of the conditions of the stability analysis theorem. Two case studies concerning the fuzzy logic control of representative chaotic systems that belong to the general class of chaotic systems are included in order to illustrate our stable design approach. A set of simulation results is given to validate the theoretical results.

Stable fuzzy logic control of a general class of chaotic systems

A combined negative selection algorithm-particle swarm optimization for an email spam detection system

[in self-defense].

This article presents a triple-valued gravitational search algorithm (TGSA) to tackle the graph planarization problem (GPP). GPP is one of the most important tasks in graph theory, and has proved to be an NP-hard problem. To solve it, TGSA uses a triple-valued encoding scheme and models the search space into a triangular hypercube quantitatively based on the well-known single-row routing representation method. The agents in TGSA, whose interactions are driven by the gravity law, move toward the global optimal position gradually. The position updating rule for each agent is based on two indices: one is a velocity index which is a function of the current velocity of the agent, and the other is a population index based on the cumulative information in the whole population. To verify the performance of the algorithm, 21 benchmark instances are tested. Experimental results indicate that TGSA can solve the GPP by finding its maximum planar subgraph and embedding the resulting edges into a plane simultaneously. Compared with traditional algorithms, a novelty of TGSA is that it can find multiple optimal solutions for the GPP. Comparative results also demonstrate that TGSA outperforms the traditional meta-heuristics in terms of the solution qualities within reasonable computational times. Published by John Wiley & Sons, Inc.

/pdf/graph-planarization-problem-optimization-based-on-triple-4aw4np6yue.pdf

Graph planarization problem optimization based on triple-valued gravitational search algorithm

The accuracy of face recognition is very important for its security in many applications, because wrong face recognition may cause such security problems as authorization. To increase the recognition rate in such face database as ORL, a face recognition algorithm should be good at minimizing the disturbances of facial pose, illumination and expression (PIE) to this recognition. In this paper, the improved clonal selection algorithm and diverse samples are designed. The improved clonal selection algorithm searches the most similar sample for unknown face image, according to the affinity between the unknown one and the most similar sample. The affinity is newly designed to improve the adaptive matching between the object and the samples. Compared with some state-of-the-art algorithms on the ORL face database, the proposed approach outperforms the other algorithms in the recognition rate, based on the experimental results.

High-precision Immune Computation for Secure Face Recognition

In this paper, a security problem of cooperative immunization against collaborative attacks such as blackhole attacks and wormhole attacks, in the mobile ad hoc networks such as the Worldwide Interoperability for Microwave Access (WiMAX) networks, was discussed. Because of the vulnerabilities of the protocol suites, collaborative attacks in the mobile ad hoc networks can cause more damages than individual attacks. In human immune system, nonselfs (i.e., viruses, bacteria and cancers etc.) can attack human body in a collaborative way and cause diseases in the human body. With the inspiration from the human immune system, a tri-tier cooperative immune model was built to detect and eliminate the collaborative attacks (i.e., nonselfs) in the mobile ad hoc networks. ARM-based Network Simulator (NS2) tests and probability analysis were utilized in the prototype for immune model to analyze and detect the attacks. Experimental results demonstrate the validation and effectiveness of the model proposed by minimizing the collaborative attacks and immunizing the mobile ad hoc networks. Copyright © 2012 John Wiley & Sons, Ltd.

/pdf/immunizing-mobile-ad-hoc-networks-against-collaborative-3vl8s99yko.pdf

Immunizing mobile ad hoc networks against collaborative attacks using cooperative immune model

In this paper, anti-virus problem and software fault diagnosis of mobile robot, an immune robot, is discussed with proposal of a novel tri-tier immune system (TTIS). TTIS is a novel artificial immune system, which is comprised of three computing tiers and based on the normal model. The three tiers include inherent immune tier, adaptive immune tier and parallel immune tier. The tri-tier immune model is built on some theories of human immune system and has many good features, such as adaptability, immunity, memory, learning, and robustness. At the same time, for such immune robot, a novel normal model for the robot software is also proposed. The normal model is built on the space---time properties of each component in the robot software and can uniquely identify the normal state of the robot software. Such tri-tier immune system based on the normal model is suitable for anti-virus and fault diagnosis, which enable the immune robot to detect all viruses and faults in the robot software, recognize many viruses and faults, eliminate the viruses and faults, and repair the damaged robot software to its normal state. Meanwhile, simulation results show that the tri-tier immune system has the properties of immunity, security and robustness.

Tri-tier Immune System in Anti-virus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model

It is known that both excitatory and inhibitory neuronal networks can achieve robust synchronization only under certain conditions, such as long synaptic delay or low level of heterogeneity. In this work, robust synchronization can be found in an excitatory/inhibitory (E/I) neuronal network with medium synaptic delay and high level of heterogeneity, which often occurs in real neuronal networks. Two effects of post-synaptic potentials (PSP) to network synchronization are presented, and the synaptic contribution of excitatory and inhibitory neurons to robust synchronization in this E/I network is investigated. It is found that both excitatory and inhibitory neurons may contribute to robust synchronization in E/I networks, especially the excitatory PSP has a more positive effect on synchronization in E/I networks than that in excitatory networks. This may explain the strong robustness of synchronization in E/I neuronal networks.

Tao Gong

Papers

Graph planarization problem optimization based on triple-valued gravitational search algorithm

High-precision Immune Computation for Secure Face Recognition

Immunizing mobile ad hoc networks against collaborative attacks using cooperative immune model

Tri-tier Immune System in Anti-virus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model

Robust Synchronization in an E/I Network with Medium Synaptic Delay and High Level of Heterogeneity