Moth flame optimization (MFO) is a swarm-based algorithm with mediocre performance and marginal originality proposed in recent years. It tried to simulate the fantasy navigation mode of moth lateral positioning. The basic MFO has no specific, deep strategies in different periods of the algorithm and a fragile evolutionary basis, which may lead to the problem of falling into local optimum and slow convergence trend. Therefore, this paper introduces a double adaptive weight mechanism into the MFO algorithm, termed as WEMFO, to boost the search capability of the basic MFO and provide a more efficient tool for optimization purposes. The proposed WEMFO adjusts the search strategy adaptively in different periods of the algorithm, making it more flexible between global search (diversification) and local search (intensification). The WEMFO algorithm is compared with some illustrious metaheuristic solvers and advanced metaheuristic methods developed in recent years on thirty benchmark functions. The experimental results expose that the developed WEMFO has apparent compensations in terms of convergence speed and solution accuracy. Moreover, this paper analyzes the diversity and balance of WEMFO and applies the algorithm to several engineering problems. The experimental results show that the WEMFO algorithm has good performance in engineering problems. Additionally, the proposed WEMFO was also applied to train Kernel Extreme Learning Machine (KELM), the resultant optimized WEMFO-KELM model was applied to six clinical disease classification problems. By comparing with MFO-KELM and other five classification models, the experimental results showed that the proposed algorithm had shown better performance in practical problems. An online guide for the algorithm in this research WEMFO and proposed classifier WEMFO-KELM will be publicly available at https://aliasgharheidari.com .

Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis

A sensor is a tool used to directly measure the test compound (analyte) in a sample. Ideally, such a device is capable of continuous and reversible response and should not damage the sample. Nanosensor refers to a system in which at least one of the nanostructures is used to detect gases, chemicals, biological agents, electric fields, light, heat, etc. in its construction. The use of nanomaterials significantly increases the sensitivity of the system. In biosensors, the part of the system used to attach to the analyte and specifically detect it is a biological element (such as a DNA strand, antibody, enzyme, whole cell). The “Nano Biosensors” series reviews various types of biosensors and biochips (including an array of biosensors), emphasizing the role of nanostructures, developed for medical and biological applications. Nano Biosensors Electrochemical sensors are sensors that use the biological element as a diagnostic component and the electrode as a transducer. The use of nanostructures in these systems is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Given the nature of the biomaterial detection process, electrochemical biosensors are divided into catalytic and propulsion. Common electrochemical techniques common in sensors include potentiometric, chronometry, voltammetry, impedance measurement, and field effect transistor (FET). Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power. The use of nanostructures in these sensors is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Various types of nanostructures including nanoparticles, nanotubes and nanowires, nanopores, self-adhesive monolayers and nanocomposites can be used to improve the performance and efficiency of sensors in their structure. Simultaneous use of the advantages of nanostructures and electrochemical techniques has led to the emergence of sensors with high sensitivity and decomposition power.

Nano Biosensors: Properties, applications and electrochemical techniques

Nanofluid heat transfer within a pipe equipped with external device

Boosting slime mould algorithm for parameter identification of photovoltaic models

The goal of the present research is an investigation of the smart control and wave propagation examination of a graphene nanoplatelets reinforced (GPLRC) cylindrical micro-shell covered with piezoe...

Intelligent wave dispersion control of an inhomogeneous micro-shell using a proportional-derivative smart controller

A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions Distributed control rules are also defined to confine the motion space of each agent for collision avoidance It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm

Convergent Multiagent Formation Control With Collision Avoidance

Object traversing by monocular UAV in outdoor environment

The problem of collaborative self-localization and target tracking method under challenge environment is studied in this paper. Specifically, the scenario with general nonlinear process and sensing model as well as sparse communication is considered by combining the distributed tracking (DT) and the collaborative localization (CL) techniques. To better characterize the statistics after nonlinear transformations, the unscented transformation (UT) approach is adopted. Simulations are extensively studied to show that the proposed method have better performance on both self-localization and target tracking than the solo CL or DT method.

Houxin Zhang

Papers

Convergent Multiagent Formation Control With Collision Avoidance

Object traversing by monocular UAV in outdoor environment

Collaborative Self-Localization and Target Tracking Under Sparse Communication