MLGSA: Multi-Leader Gravitational Search Algorithm for Multi-Objective Optimization Problem

Recently, we have introduced Multi-Leader Particle Swarm Optimization (MLPSO) algorithm for multiobjective optimization problem. Better convergence and diversity have been observed over the conventional MultiObjective Particle Swarm Optimization. In this paper, the same concept is extended to Gravitational Search Algorithm (GSA). The performance was investigated by solving a set of ZDT test problem. An analysis was also performed by varying the value of initial gravitational constant

Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare

Nature-Inspired Computing or NIC for short is a relatively young field that tries to discover fresh methods of computing by researching how natural phenomena function to find solutions to complicated issues in many contexts. As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary. In the domains of biology, physics, engineering, economics, and management, NIC techniques are used. In real-world classification, optimization, forecasting, and clustering, as well as engineering and science issues, meta-heuristics algorithms are successful, efficient, and resilient. There are two active NIC patterns: the gravitational search algorithm and the Krill herd algorithm. The study on using the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in medicine and healthcare is given a worldwide and historical review in this publication. Comprehensive surveys have been conducted on some other nature-inspired algorithms, including KH and GSA. The various versions of the KH and GSA algorithms and their applications in healthcare are thoroughly reviewed in the present article. Nonetheless, no survey research on KH and GSA in the healthcare field has been undertaken. As a result, this work conducts a thorough review of KH and GSA to assist researchers in using them in diverse domains or hybridizing them with other popular algorithms. It also provides an in-depth examination of the KH and GSA in terms of application, modification, and hybridization. It is important to note that the goal of the study is to offer a viewpoint on GSA with KH, particularly for academics interested in investigating the capabilities and performance of the algorithm in the healthcare and medical domains.Comment: 35 page

Non-dominated sorting gravitational search algorithm for multi-objective optimization of power transformer design

Transformers are crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower mass and losses. Multi-objective Optimization Problems (MOPs) consist of several competing and incommensurable objective functions. Recently, as a search optimization technique inspired by nature, evolutionary algorithms have been broadly applied to solve MOPs. In this paper, a power Transformer Design (TD) methodology using Non-dominated Sorting Gravitational Search Algorithm (NSGSA) is proposed. Results are obtained and presented for NSGSA approach. The obtained results for the study case are compared with those results obtained when using other multi objective optimization algorithms which are Novel Gamma Differential Evolution (NGDE) Algorithm, Chaotic Multi-Objective Algorithm (CMOA), and Multi- Objective Harmony Search (MOHS) algorithm. From the analysis of the obtained results, it has been concluded that NSGSA algorithm provides the most optimum solution and the best results in terms of normalized arithmetic mean value of two objective functions using NSGSA to the TD optimization

Force-based Cooperative Search Directions in Evolutionary Multi-objective Optimization

International audienceIn order to approximate the set of Pareto optimal solutions, several evolutionary multi-objective optimization (EMO) algorithms transfer the multi-objective problem into several independent single-objective ones by means of scalarizing functions. The choice of the scalarizing functions' underlying search directions, however, is typically problem-dependent and therefore difficult if no information about the problem characteristics are known before the search process. The goal of this paper is to present new ideas of how these search directions can be computed \emph{adaptively} during the search process in a \emph{cooperative} manner. Based on the idea of Newton's law of universal gravitation, solutions attract and repel each other \emph{in the objective space}. Several force-based EMO algorithms are proposed and compared experimentally on general bi-objective $\rho$MNK landscapes with different objective correlations. It turns out that the new approach is easy to implement, fast, and competitive with respect to a $(\mu+\lambda)$-SMS-EMOA variant, in particular if the objectives show strong positive or negative correlations

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

A new method for generating initial solutions of capacitated vehicle routing problems

In vehicle routing problems, the initial solutions of the routes are important for improving the quality and solution time of the algorithm. For a better route construction algorithm, the obtained initial solutions must be basic, fast, and flexible with reasonable accuracy. In this study, initial solutions improvement for CVRP is introduced based on a method that is introduced in the literature. Using a different formula for addressing the gravitational forces, a new method is introduced and compared with the previous physics inspired algorithm. By using the initial solutions of the proposed method and using them as RTR and SA initial routes, it is seen that better results are obtained when compared with various algorithms from the literature. Also, in order to fairly compare the algorithms executed on different machines, a new comparison scale for the solution quality of vehicle routing problems is proposed that depends on the solution time and the deviation from the best known solution. The obtained initial solutions are then input to Record-to-Record and Simulated Annealing algorithms to obtain final solutions. Various test instances and CVRP solutions from the literature are used for comparison. The comparisons with the proposed method have shown promising results

Improved particle swarm optimization and gravitational search algorithm for parameter estimation in aspartate pathways

One of the main issues in biological system is to characterize the dynamic behaviour of the complex biological processes. Usually, metabolic pathway models are used to describe the complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. Therefore, the parameter values are estimated by fitting the model with experimental data. However, the estimation on these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Previously, a computational approach namely optimization algorithms are used to estimate the measurement of the model parameters. Most of these algorithms previously often suffered bad estimation for the biological system models, which resulted in bad fitting (error) the model with the experimental data. This research proposes a parameter estimation algorithm that can reduce the fitting error between the models and the experimental data. The proposed algorithm is an Improved Particle Swarm Optimization and Gravitational Search Algorithm (IPSOGSA) to obtain the near-optimal kinetic parameter values from experimental data. The improvement in this algorithm is a local search, which aims to increase the chances to obtain the global solution. The outcome of this research is that IPSOGSA can outperform other comparison algorithms in terms of root mean squared error (RMSE) and predictive residual error sum of squares (PRESS) for the estimated results. IPSOGSA manages to score the smallest RMSE with 12.2125 and 0.0304 for Ile and HSP metabolite respectively. The predicted results are benefits for the estimation of optimal kinetic parameters to improve the production of desired metabolites

EMCSO: An Elitist Multi-Objective Cat Swarm Optimization

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optimization (CSO), a swarm-based algorithm with ability of exploration and exploitation, to produce offspring solutions and uses thenon-dominated sorting method to findthe solutionsas close as to POFand crowding distance technique toobtain a uniform distribution among thenon-dominated solutions. Also, the algorithm is allowedto keep the elites of population in reproduction processand use an opposition-based learning method for population initialization to enhance the convergence speed.The proposed algorithm is tested on standard test functions (zitzler’ functions: ZDT) and its performance is compared with traditional algorithms and is analyzed based onperformance measures of generational distance (GD), inverted GD, spread,and spacing. The simulation results indicate that the proposed method gets the quite satisfactory results in comparison with other optimization algorithms for functions of ZDT1 and ZDT2. Moreover, the proposed algorithm is applied to solve multi-objective knapsack problem

Recovery Act: Multi-Objective Optimization Approaches for the Design of Carbon Geological Sequestration Systems

The main objective of this project is to provide training opportunities for two graduate students in order to improve the human capital and skills required for implementing and deploying carbon capture and sequestration (CCS) technologies. The graduate student effort will be geared towards the formulation and implementation of an integrated simulation-optimization framework to provide a rigorous scientific support to the design CCS systems that, for any given site: (a) maximize the amount of carbon storage; (b) minimize the total cost associated with the CCS project; (c) minimize the risk of CO2 upward leakage from injected formations. The framework will stem from a combination of data obtained from geophysical investigations, a multiphase flow model, and a stochastic multi-objective optimization algorithm. The methodology will rely on a geostatistical approach to generate ensembles of scenarios of the parameters that are expected to have large sensitivities and uncertainties on the model response and thus on the risk assessment, in particular the permeability properties of the injected formation and its cap rock. The safety theme will be addressed quantitatively by including the risk of CO2 upward leakage from the injected formations as one the objectives that should be minimized in the optimization problem. The research performed under this grant is significant to academic researchers and professionals weighing the benefits, costs, and risks of CO2 sequestration. Project managers in initial planning stages of CCS projects will be able to generate optimal tradeoff surfaces and with corresponding injection plans for potential sequestration sites leading to cost efficient preliminary project planning. In addition, uncertainties concerning CCS have been researched. Uncertainty topics included Uncertainty Analysis of Continuity of Geological Confining Units using Categorical Indicator Kriging (CIK) and the Influence of Uncertain Parameters on the Leakage of CO2 to Overlying Formations. Reductions in uncertainty will lead to safer CCS projects