Group Search Optimization Algorithm Research Articles

A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

Producer pre-selection mechanism based on self-adaptive group search optimizer

To overcome the prematurity of Group Search Optimizer( GSO) and improve its convergence speed, a producer pre-selection mechanism based self-adaptive group search optimizer( PSAGSO) algorithm was proposed. Firstly, the reverse mutation operator and pre-selection mechanism were employed to generate a new producer by producer-scrounger model to guide the search directions of scrounger and effectively maintain the diversity of population. Secondly, a self-adaptive method based on linear decreasing weight was adopted to adjust the proportion of rangers, which is to improve individual vigor of the population and benefit to escape from local optima. Experiments were conducted on a set of 12 benchmark functions. For 30-dimensional function optimization, the test data obtained by the PSAGSO algorithm was better than that in the literature( HE S, WU Q H, SAUNDERS J R. Group search optimizer: an optimization algorithm inspired by animal searching behavior.IEEE Transactions on Evolutionary Computation, 2009, 13( 5) : 973- 990). For 300-dimensional numerical optimization problems, the PSAGSO algorithm exhibited better performance. The experimental result demonstrates that the PSAGSO algorithm improves the group search optimizer, and to some extent it improves the algorithm convergence speed and accuracy.

Group path planning method based on improved group search optimization algorithm

Concerning the problems that traditional path planning of group animation needs long time for searching and is of poor optimization,the authors proposed a multi-threaded path planning algorithm based on group search optimization.Firstly,to solve the problem that the algorithm easily gets trapped in local optimum,metroplis rule was introduced in this search mode.Secondly,by using random path through the multi-threading and stitching techniques,the algorithm was applied to path planning.The simulation results show that the algorithm has better global convergence both in high-dimensional and low-dimensional cases,and the method is good enough to meet the requirements of path planning in complex animation environment.

A modified group search optimiser for constrained optimisation problems

The purpose of this study is to propose a modified group search optimiser (GSO) algorithm for constrained optimisation problems. Three strategies are designed to improve the performance of GSO algorithm. Levy flight strategy is employed for the producer to improve its exploitation ability, and also to simplify the computation. To further improve its exploitation ability, dimension-by-dimension search strategy is used to tackle the dimensions interference problem when producer uses levy flight strategy to find new position. Mutation strategy with adaptive controlling probability is used to improve the exploration ability by keeping the diversity of population. Using the superiority of feasible point scheme and the parameter free penalty scheme to handle constraints, the modified GSO algorithm was tested on 13 well-known benchmark problems. Comparisons are made with the standard GSO algorithm and other algorithms which use penalty function and various constraint handling methods. Results show the modified GSO algorithm is effective and competitive for constrained optimisation problems.

Continuous quick group search optimizer for solving non-convex economic dispatch problems

In this paper a new optimization algorithm is applied for solution of non-convex and large scale economic dispatch (ED) problems. A continuous version of quick group search optimizer (QGSO) algorithm is proposed. A more realistic ED formulation with considering valve-points effect, prohibited operating zones, transmission losses and ramp-rate limits is utilized. The performance of the proposed method is verified by implementation on five test systems. Case studies and numerical examples show that the proposed continuous QGSO algorithm has better solution quality comparing with the most of the latest reported algorithms in the literature.

New trends on bio-inspired computation

Biological systems are self-organizing, tolerant of manufacturing defects and they adapt, rather than being programmed, to their environments. The problems they solve involve the interaction of an organism/system with the real world. Bio-inspired computation is the use of computers to model nature, and simultaneously the study of natural biological systems to improve the usage of computers. It is a major subset of natural computation. Recently, bio-inspired computation, such as evolutionary computation, swarm intelligence bacterial foraging, cultural algorithms, neural networks, fuzzy systems, rough sets, molecular computing and other bionics, is becoming increasingly important in face of the complexity of today’s demanding applications. This special issue on bio-inspired computation is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends in bio-inspired computation. After a detailed review process, eight papers were selected to reflect the thematic vision. The contents of these studies are briefly described as follows. The group search optimizer (GSO) was inspired by animal social searching behaviour. It has been shown that the global search performance of the GSO is competitive to other biologically inspired optimization algorithms. In the paper, ‘Analysis of premalignant pancreatic cancermass spectrometry data for biomarker selection using a group search optimizer’, S. He et al. apply a GSO as a feature selection method to mass spectrometry (MS) data analysis for premalignant pancreatic cancer biomarker discovery. After applying a smooth nonlinear energy operator (SNEO) to detect peaks, then a GSO with linear discriminant analysis (LDA) is used to select a parsimonious set of peak windows ((biomarkers) that can distinguish cancer. After selecting a set of biomarkers, a support vector machine (SVM) is then applied to build a classifier to diagnosis premalignant cancer cases. The GSO algorithm was compared with a genetic algorithm (GA), evolution strategies (ES), evolutionary programming (EP) and a particle swarm optimizer (PSO). The results showed that the GSO-based feature selection algorithm is capable of selecting a parsimonious set of biomarkers to achieve better classification performance than other algorithms. In the paper, ‘Viral system algorithm: foundations and comparison between selective and massive infections’, Pablo Cortes et al. present a guided and deep introduction to viral systems (VS), a novel bio-inspired methodology based on the natural biological process taking part when the organism has to give a response to an external infection. VS has proven to be very efficient when dealing with problems of high complexity. The paper discusses on the foundations of viral systems, presents the main pseudocodes that need to be implemented and illustrates the methodology application. A comparison between VS and other metaheuristics, as well between different VS approaches, is presented. Finally, trends and new research opportunities are presented for this bio-inspired methodology. Population-based heuristic optimization methods like differential evolution (DE) depend largely on the generation of the initial population. The initial population not only affects the search for several iterations but often also has an influence on the final solution. The conventional method for generating an initial population is the use of computergenerated pseudorandom numbers, which may not be very effective. In the paper entitled with ‘A simplex differential evolution algorithm: development and applications’, Musrrat Ali et al. have investigated the potential of generating the initial population by integrating the non-linear simplex method (NSM) of Nelder and Mead with pseudorandom numbers in a DE algorithm. The resulting algorithm named the non-linear simplex differential evolution (NSDE) is tested on a set of 20 benchmark problems with box constraints and two real-life problems. Numerical results show that the proposed scheme for generating the random numbers significantly improves the performance of DE in terms of fitness function value, convergence rate and average CPU time. In the paper, ‘How to design a powerful family of particle swarm optimizers in inverse modelling’, Juan Luis Fernandez Martinez et al. show how to design a powerful set of particle swarm optimizers for application in inverse modelling.

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.

An improved group search optimizer with operation of quantum-behaved swarm and its application

Group search optimizer (GSO) is a novel swarm intelligent (SI) algorithm for continuous optimization problem. The framework of the algorithm is mainly based on the producer–scrounger (PS) model. Comparing with ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms, GSO emphasizes more on imitating searching behavior of animals. In standard GSO algorithm, more than 80% individuals are chosen as scroungers, and the producer is the one and only destination of them. When the producer cannot found a better position than the old one in some successive iterations, the scroungers will almost move to the same place, the group might be trapped into local optima though a small quantity of rangers are used to improve the diversity of it. To improve the convergence performance of GSO, an improved GSO optimizer with quantum-behaved operator for scroungers according to a certain probability is presented in the paper. In the method, the scroungers are divided into two parts, the scroungers in the first part update their positions with the operators of QPSO, and the remainders keep searching for opportunities to join the resources found by the producer. The operators of QPSO are utilized to improve the diversity of population for GSO. The improved GSO algorithm (IGSO) is tested on several benchmark functions and applied to train single multiplicative neuron model. The results of the experiments indicate that IGSO is competitive to some other EAs.

Group search optimizer based optimal location and capacity of distributed generations

This paper presents a novel efficient population-based heuristic approach for optimal location and capacity of distributed generations (DGs) in distribution networks, with the objectives of minimization of fuel cost, power loss reduction, and voltage profile improvement. The approach employs an improved group search optimizer (iGSO) proposed in this paper by incorporating particle swarm optimization (PSO) into group search optimizer (GSO) for optimal setting of DGs. The proposed approach is executed on a networked distribution system—the IEEE 14-bus test system for different objectives. The results are also compared to those that executed by basic GSO algorithm and PSO algorithm on the same test system. The results show the effectiveness and promising applications of the proposed approach in optimal location and capacity of DGs.

Analysis of premalignant pancreatic cancer mass spectrometry data for biomarker selection using a group search optimizer

This paper presents a novel group search optimizer (GSO)-based biomarker discovery method for pancreatic cancer diagnosis using mass spectrometry (MS) data. The GSO was inspired by animal social searching behaviour. It has been shown that the global search performance of the GSO is competitive to other biologically inspired optimization algorithms. In this study, we applied a GSO as a feature selection method to MS data analysis for premalignant pancreatic cancer biomarker discovery. We first applied a smooth non-linear energy operator to detect peaks. Then a GSO with linear discriminant analysis was used to select a parsimonious set of peak windows (biomarkers) that can distinguish cancer. After selecting a set of biomarkers, a support vector machine was then applied to build a classifier to diagnosis premalignant cancer cases. We compared the GSO algorithm with a genetic algorithm, evolution strategies, evolutionary programming and a particle swarm optimizer. The results showed that the GSO-based feature selection algorithm is capable of selecting a parsimonious set of biomarkers to achieve better classification performance than other algorithms. The source code of the proposed GSO-based feature selection algorithm is available at www.cs.bham.ac.uk/~szh .

New group search optimizer algorithm based on chaotic searching

To improve the performance of Group Search Optimizer(GSO),a new group search optimizer algorithm based on Chaotic Group Search Optimizer(CGSO) in combination with the global searching characteristic of the chaos method was proposed in the paper.In the method,the good position of producer was updated by chaotic searching,the new position of scrounger was determined by the position of producer and the best position which it had been achieved so far,and the new position of rangers was achieved by chaotic mutation.The global convergent performance of GSO was improved by using the initial sensitivity of the Logistic map to expand the scope of the search and by employing the global ergodicity to search the positions.Four function optimization problems were simulated by CGSO and GSO.The experimental results indicate that CGSO is more effective than the others.

The Particle Swarm Group Search Optimization Algorithm and Its Application on Structural Design

The Particle Swarm Group Search Optimization Algorithm and Its Application on Structural Design

The Group Search Optimizer and its Application to Truss Structure Design

This paper introduces a novel optimization algorithm, group search optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO is used to investigate the truss structures with continuous variables and was tested by three truss structure optimization problems. The optimization results were compared with those of the particle swarm optimizer (PSO), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO). The calculation results show that the GSO has preferable convergence rate and accuracy. Results from the three tested cases illustrate the competitive ability of the GSO to find the optimal results. It is desired for GSO to be used for structural optimal design problems.

Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for “finding” (producer) or for “joining” (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving. </para>

Breast cancer diagnosis using an artificial neural network trained by group search optimizer

This paper presents a novel optimization algorithm: a group search optimizer (GSO) for training an artificial neural network (ANN) used for diagnosis of breast cancer. The GSO is inspired by animal social searching behaviour. Its global search performance has been proved competitive to other evolutionary algorithms and the particle swarm optimizer. The parameters of a three-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Wisconsin diagnostic breast cancer data from the UCI Machine Learning repository are employed as a benchmark classification problem to evaluate the proposed method. In comparison with other sophisticated machine learning techniques used for ANN training, including some ANN ensembles, the GSO for ANN, GSOANN, has a better convergence rate and generalization performances for the breast cancer diagnosis problem.