Galaxy-based Search Algorithm Research Articles

An adaptation of the galaxy-based search algorithm for solving the single machine total weighted tardiness problem

The single machine total weighted tardiness (SMTWT) problem (referred to as 1//ΣwiTi) requires a given set of jobs to be sequenced on a single machine while minimising the sum of weighted tardiness, where the tardiness of a job is zero if it is completed before its due date and is equal to completion time minus its due date otherwise. In this article, the 'galaxy-based search algorithm' (GbSA) is adapted to solve the SMTWT benchmark problems and its results are compared with the best-known reported solutions. The GbSA is a newly developed optimisation tool and is considered a metaheuristic technique inspired by the dynamics of galactic arm spirals. It is robust, capable of avoiding being trapped in a local optimum, has a relative fast convergence, and is easily implementable. The experiments results show that the proposed adaptation of the GbSA is effective and obtains high-quality solutions within reasonable computation times. [Submitted 4 March 2019; Accepted 26 February 2020]

An adaptation of the Galaxy-based Search Algorithm for solving the Single Machine Total Weighted Tardiness Problem

An adaptation of the Galaxy-based Search Algorithm for solving the Single Machine Total Weighted Tardiness Problem

The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.

RETRACTED: Dynamic Modeling and Controller Design of DSTATCOM in a Microgrid Based on Combination of Fuzzy Set and Galaxy-based Search Algorithm

RETRACTED: Dynamic Modeling and Controller Design of DSTATCOM in a Microgrid Based on Combination of Fuzzy Set and Galaxy-based Search Algorithm

A novel fgbsa (fuzzy galaxy based search algorithm) for multi-objective reconfiguration of distribution systems

A novel fgbsa (fuzzy galaxy based search algorithm) for multi-objective reconfiguration of distribution systems

A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm

An important problem in tracking methods is how to manage the changes in object appearance, such as illumination changes, partial/full occlusion, scale, and pose variation during the tracking process. In this paper, we propose an occlusion free object tracking method together with a simple adaptive appearance model. The proposed appearance model which is updated at the end of each time step includes three components: the first component consists of a fixed template of target object, the second component shows rapid changes in object appearance, and the third one maintains slow changes generated along the object path. The proposed tracking method not only can detect occlusion and handle it, but also it is robust against changes in the object appearance model. It is based on particle filter which is a robust technique in tracking and handles non-linear and non-Gaussian problems. We have also employed a meta-heuristic approach that is called Modified Galaxy based Search Algorithm (MGbSA), to reinforce finding the optimum state in the particle filter state space. The proposed method was applied to some benchmark videos and its results were satisfactory and better than results of related works.

Dynamic Modeling and Controller Design of Distribution Static Compensator in a Microgrid Based on Combination of Fuzzy Set and Galaxy-based Search Algorithm

This paper presents a nonlinear controller for a Distribution Static Compensator (DSTATCOM) of a microgrid incorporating the Distributed Generation (DG) units. The nonlinear control has been designed based on partial feedback linearization theory and Proportional-Integral-Derivative (PID) controllers try to adjust the voltage and trace the output. This paper has proposed a combination of a fuzzy system and Galaxy-based Search Algorithm (GbSA) to optimize the parameters of the PID controllers. The results confirm that the characteristics of the response of the proposed controller (i.e. settling and rise times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM) is significantly improved by finding a high-quality solution. The proposed hybrid tuning method for the Partial Feedback Linearizing (PFL) controller concluded a better DC voltage regulation for the capacitor within the DSTATCOM. Furthermore, in the event of fault the proposed controller tuned by the fuzzy-GbSA method has shown a better performance in comparison with the conventional controller or controllers tuned by Genetic Algorithm (GA) or Particle Swarm Optimization (PSO) methods on both fault duration and after clearing times.

Novel FGbSA: Fuzzy-Galaxy-based search algorithm for multi-objective reconfiguration of distribution systems

Reconfiguration according to different criteria is an important problem in distribution systems. This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the Galaxy-based Search Algorithm (GbSA). To avoid the convergence problem, the input and output data are normalized in the same range using fuzzy sets. The main objectives of the proposed algorithm have been considered as power loss reduction, voltage profile improvement and increase of the system load balancing. The proposed technique has been investigated using the IEEE 33-bus test system and a real distribution network i.e. Tai-Power 11.4-kV distribution system. The obtained results revealed the superiority of the proposed fuzzy-GbSA method in terms of accuracy compared to the GbSA and other intelligent search algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). Furthermore, the proposed algorithm efficiently converged to the optimum solution compared to the other intelligent counterpart algorithms.

An object tracking method using modified galaxy-based search algorithm

Abstract Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in this field of study. In this paper, we have proposed an object tracking method based on a meta-heuristic approach. Although there are some meta-heuristic approaches in the literature, we have modified GbSA (galaxy based search algorithm) which is more precise than related works. The GbSA searches the state space by simulating the movement of the spiral galaxy to find the optimum object state. The proposed method searches each frame of video with particle filter and the MGSbA in a similar manner. It receives current frame and the temporal information that is related to previous frames as input and tries to find the optimum object state in each one. The experimental results show the efficiency of this algorithm in comparison with results of related methods.

Disparity Line Utilization Factor and Galaxy-based Search Algorithm for Advanced Congestion Management in Power Systems

In this paper a new approach has been used for finding the optimal location for placing the Interline Power Flow Controller (IPFC). The IPFC is used to reduce the system loss and power flow in the heavily loaded lines and improve stability of the system. Here a new method, the Disparity Line Utilization Factor (DLUF) is used for determining the optimal placement of the (IPFC) to control the congestion in transmission lines. The DLUF ranks the transmission lines in terms of line congestion. The IPFC is accordingly placed in between the most congested and the least congested line connected to the same bus. The Galaxy-Based Search Algorithm (GBSA) is used for the optimal sizing of the IPFC. The results obtained by the proposed algorithm have been compared with that of the Genetic Algorithm (GA) to ascertain the effectiveness of the proposed method on the power system performance. The proposed method is tested on an IEEE 30 bus test system. It has been shown that by using the GBSA, the tuning of the IPFC further reduces congestion in the system by about 15%.

Optimal Power Flow with Static VAR Compensator Using Galaxy Based Search Algorithm to Minimize Real Power Losses

In this paper, Galaxy based search algorithm (GbSA) is used to solve multi-objective problem of optimization in power systems. The proposed GbSA resembles the spiral arms of some galaxies to search for the optimal solutions. The GbSA also uses a modified Hill Climbing algorithm as a local search. Simulation results show that the GbSA finds the optimal or very near optimal values in all runs of the algorithm. The weighted sum technique with equal weights has been chosen to solve the multi-objective function. The functions considered are to minimize the power losses in transmission line, cost of the real power generation and voltage deviation. Static VAR Compensator (SVC) is used for the purpose of optimal power flow. L-index is used to identify the optimal location to place SVC. The results have been compared with Genetic algorithm (GA) for IEEE-14 System.

Application of a Galaxy-Based Search Algorithm to MIMO System Capacity Optimization

The objective of this paper is to use the recently proposed galaxy-based search optimization algorithm to enhance the capacity of a multiple input multiple output (MIMO) system with rectangular arrays at both communication ends (transmitter and receiver). This new optimization tool has been recently introduced and is a metaheuristic technique inspired by the dynamics of galactic arm spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The idea is to extend the results obtained for the one-dimensional array geometry to the two-dimensional case. The purpose is to find out which array geometrical dimensions produce the highest capacity value. Compared to the linear array case, promising capacity values are found using the two-dimensional arrays which suggests their deployment in future MIMO communication systems.

Enhanced leader PSO (ELPSO): A new algorithm for allocating distributed TCSC’s in power systems

Allocation of flexible AC transmission systems (FACTS) devices is a challenging power system problem. This paper proposes a new particle swarm optimisation (PSO) variant, called enhanced leader PSO (ELPSO), for solving this problem. This algorithm is capable of solving FACTS allocation problem in a way leading to lower amounts of power flow violations, voltage deviations and power losses with respect to other optimisation algorithms. Distributed thyristor controlled series compensators (D-TCSC’s) are used. D-TCSC’s are installed at all branches except those with regulating transformers. The reactances of D-TCSC’s are found in optimisation process. ELPSO features a five-staged successive mutation strategy which mitigates premature convergence problem of conventional PSO. ELPSO and other optimisation algorithms are applied to IEEE 14 bus and 118 bus power systems for N-1 contingencies and also for simultaneous outage of four branches. The results show that it leads to lower amounts of power flow violations, voltage deviations and power losses with respect to conventional PSO (CPSO) and eight other optimisation algorithms including genetic algorithm (GA), gravitational search algorithm (GSA), galaxy based search algorithm (GBSA), invasive weed optimisation (IWO), asexual reproduction optimisation (ARO), threshold acceptance (TA), pattern search and nonlinear programming (NLP).

Multilevel Thresholding for Image Segmentation using the Galaxy-based Search Algorithm

In this paper, image segmentation of gray- level images is performed by multilevel thresholding. The optimal thresholds for this purpose are found by maximizing the between-class variance (the Otsu's criterion). The optimization (maximization) is conducted by a novel nature-inspired search algorithm, which is called Galaxy-based Search Algorithm or GbSA. The proposed GbSA is a metaheuristic for continuous optimization. It resembles the spiral arms of some galaxies to search for the optimal thresholds. The GbSA also uses a modified Hill Climbing algorithm as a local search. The GbSA also utilizes chaos for improving its performance, which is implemented by the logistic map. Experimental results show that the GbSA finds the optimal or very near optimal thresholds in all runs of the algorithm.

Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation

In this paper, the principal components analysis (PCA) is formulated as a continuous optimisation problem. Then, a novel metaheuristic inspired from nature is employed to explore the search space for the optimum solution to the PCA problem. The new metaheuristic is called 'galaxy-based search algorithm' or 'GbSA'. The GbSA imitates the spiral arm of spiral galaxies to search its surrounding. This spiral movement is enhanced by chaos to escape from local optimums. A local search algorithm is also utilised to adjust the solution obtained by the spiral movement of the GbSA. Experimental results demonstrate that the proposed GbSA for the PCA or GbSA-PCA is a promising tool for the PCA estimation.