A review of stimuli-responsive shape memory polymer composites

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

The food traceability regulations of 21 Organization for Economic Co-Operation and Development (OECD) countries were examined with attention to whether these regulations are comprehensive for all food commodities and processed foods. The countries were evaluated based on responses to a series of questions that were developed to allow assessment of their traceability programs. The questions sought background information on whether: mandatory traceability regulation(s) exists at the national level within a given country; regulations include imported products, and the nature of required documentation for imports; an electronic database(s) for traceability exists and, if present, its accessibility; and labeling regulations allow consumer access and understanding of traceability. The examination ranked the countries that have specific traceability regulations for all commodities, both domestic and imports, as "Progressive," while countries with less broad or stringent regulations were ranked as "Moderate," and countries that were still in the developmental stage of mandatory or industry-led traceability requirements were ranked as "Regressive." Aggregate scores were developed from all of the rankings, determined on the basis of the questions, for each of the 21 countries, to provide an overall world ranking score. The aggregate scores were "Superior," "Average," or "Poor."

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Comparison of Global Food Traceability Regulations and Requirements

A review on the fatigue behavior of Ti-6Al-4V fabricated by electron beam melting additive manufacturing

The assessment of pollution levels of heavy metals soil contamination is significant to human health and environmental management. The purpose of this article is to apply two methods, which are Principal component analysis (PCA) and Geoaccumulation index ( I geo ), to assess heavy metals contamination levels in the area around copper mine tailing, and to compare the results with Hakanson potential ecological risk index techniques (RI). The rank of soil Cd pollution levels, which is assessed using I geo , is consistent with the one by RI, while the PCA assessments result of comprehensive contamination level in soil discrepancy with RI and I geo . PCA concerned with the distribution of all elements in soil, while I geo and RI are mainly determined by the elements with high concentration or big Toxic Response Factor value. The combined application of PCA and I geo can effectively identify the comprehensive and single pollution levels of elements in soil, thus important to the extent determination of heavy metals pollution in soil.

Assessment of Soil Heavy Metal Pollution with Principal Component Analysis and Geoaccumulation Index

This paper aims to compare the material removal rate, ν between a Dimensional Analysis (DA) model, an Artificial Neural Network (ANN) model and an experimental result for a low gap current of an Electrical Discharge Machining (EDM) process. The data analysis is based on a copper electrode and steel workpiece materials. The DA and ANN model that have been developed and reported earlier by authors are used to compare the material removal of EDM process. The result indicated that the ANN model provides better accuracy towards the experimental results.

Comparison Studies of Electrical Discharge Machining (EDM) Process Model for Low Gap Current

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

Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. A variant of PSO, namely, binary particle swarm optimization (BinPSO) has been previously developed to solve discrete optimization problems. Later, many studies have been done to improve BinPSO in term of convergence speed, stagnation in local optimum, and complexity. In this paper, a novel multi-state particle swarm optimization (MSPSO) is proposed to solve discrete optimization problems. Instead of evolving a high dimensional bit vector as in BinPSO, the proposed MSPSO mechanism evolves states of variables involved. The MSPSO algorithm has been applied to two benchmark instances of traveling salesman problem (TSP). The experimental results show that the the proposed MSPSO algorithm consistently outperforms the BinPSO in solving the discrete combinatorial optimization problem.

A Novel Multi-state Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

Electrical Discharge Machining (EDM) is included in a stochastic process. So maintaining gap between electrode and workpiece is not easy. In order to control the gap, a proportional integral derivative (PID) controller is designed for EDM servo actuator system. The main goal of this work is to get PID parameters through Particle Swarm Optimization (PSO) algorithm to ensure a stable, robust and controlled system. The controller and the model for EDM die sinking are verified by simulation of the control system using MATLAB and simulink program. Simulation results verify the effectiveness of the PID controller in which its parameter determined by PSO to control the electrode position towards workpiece.

PID controller tuning by particle swarm optimization on electrical discharge machining servo control system

This paper presents a modified Gravitational Search Algorithm (GSA) called Discrete Gravitational Search Algorithm (DGSA) for discrete optimization problems. In DGSA, 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 are used to evaluate the performance of the proposed DGSA. The experimental result shows that the FDGSA able to find better solutions and converges faster compared to the Binary Gravitational Search Algorithm.

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Muhammad Arif Abdul Rahim

Papers

Comparison Studies of Electrical Discharge Machining (EDM) Process Model for Low Gap Current

A Fast Discrete Gravitational Search Algorithm

A Novel Multi-state Particle Swarm Optimization for Discrete Combinatorial Optimization Problems

PID controller tuning by particle swarm optimization on electrical discharge machining servo control system

A Modified Gravitational Search Algorithm for Discrete Optimization Problem