A survey on new generation metaheuristic algorithms

The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area. In this work, opposition-based learning (OBL) is combined with GWO to enhance its exploratory behavior while maintaining a fast convergence rate. Spearman's correlation coefficient is used to determine the omega (ω) wolves (wolves with the lowest social status in the pack) on which to perform opposition learning. Instead of opposing all the dimensions in the wolf, a few dimensions of the wolf are selected on which opposition is applied. This assists with avoiding unnecessary exploration and achieving a fast convergence without deteriorating the probability of finding optimum solutions. The proposed algorithm is tested on 23 optimization functions. An extensive comparative study demonstrates the superiority of the proposed method. The source code for this algorithm is available at "https://github.com/dhargupta-souvik/sogwo"

Selective Opposition based Grey Wolf Optimization

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.

A review of task scheduling based on meta-heuristics approach in cloud computing

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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Ant Lion Optimization: Variants, Hybrids, and Applications

In this paper, we present a new algorithm binary discrete optimization method based on cat swarm optimization (CSO). BCSO is a binary version of CSO generated by observing the behaviors of cats. As in CSO, BCSO consists of two modes of operation: tracing mode and seeking mode. The BCSO presented in this paper is implemented on a number of benchmark optimization problems and zero-one knapsack problem. The obtained results are compared with a number of different optimization problems including genetic algorithm and different versions of binary discrete particle swarm optimization. It is shown that the proposed method greatly improves the results obtained by other binary discrete optimization problems.

Discrete binary cat swarm optimization algorithm

This paper presents a novel optimization algorithm based on competitive behavior of various creatures such as birds, cats, bees and ants to survive in nature. In the proposed method, a competition is designed among all aforementioned creatures according to their performances. Every optimization algorithm can be appropriate for some objective functions and may not be appropriate for another. Due to the interaction between different optimization algorithms proposed in this paper, the algorithms acting based on the behavior of these creatures can compete each other for the best. The rules of competition between the optimization methods are based on imperialist competitive algorithm. Imperialist competitive algorithm decides which of the algorithms can survive and which of them must be extinct. In order to have a comparison to well-known heuristic global optimization methods, some simulations are carried out on some benchmark test functions with different and high dimensions. The obtained results shows that the proposed competition based optimization algorithm is an efficient method in finding the solution of optimization problems.

COOA: Competitive optimization algorithm

Optimization methods based on swarm intelligence, have been used widely in science. These methods are mainly inspired from swarm behavior of animals in nature. Grey Wolf Optimizer (GWO) is a meta-heuristic approach simulating wolves' behavior while they are hunting. In this research, it has been tried to improve the final results of the original version of algorithm, compared with other common optimization approaches, using the techniques of opposition-based learning and parallelism. The obtained results from implementation and performing the improved algorithm on well-known benchmark functions indicate enhancement the convergence speed and precision in final results.

A parallel grey wolf optimizer combined with opposition based learning

The brain emotional learning algorithm inspired by a reduced system of a computational model simulates the brain learning performance quite simply with mimicking mammalian brains. The present paper endeavors to come forward to an improved model of the emotional learning algorithm based on the interval knowledge. In this proposed model, based on the interval knowledge, the weights of the amygdala and orbitofrontal sections will be updated. Eventually, the results of implementing and performing the improved brain emotional learning algorithm will be presented to be compared with the original version of the algorithm to Prediction the chaotic time series, Lorenz and Rossler, about which a noticeable improvement in its precision, accuracy and speed of convergence of the final results is reported.

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An Improved Model Of Brain Emotional Learning Algorithm Based On Interval Knowledge

Today, neural systems are being applied as a powerful tool to solve a host of problems. The more computational model of the presented neural system and the neural system of animals look alike, the more powerful the computational model would be. The brain emotional learning algorithm in the Amygdala inspired by Limbic of mammalian brains was put forward originally by Mor ́en. A combination of some Limbic systems as some living creatures will be applied in this paper. The gathering of these living creatures and output summation of each Limbic system in accordance with the relevant applied coefficient will result in social intelligence. The proposed model has been compared with feed-forward back propagation network, Elman back propagation network, and a computational model of emotional learning in the Amygdala to forecast Mackey-Glass Chaotic Time Series. The results of the above mentioned comparison concludes that in forecasting Mackey-Glass Chaotic Time Series, the computational model of social intelligence based on the emotional learning in the Amygdala has shown fewer errors in not only training patterns but also testing patterns.

/pdf/computational-model-of-social-intelligence-based-on-5guk238eko.pdf

Yousef Sharafi

Papers

Discrete binary cat swarm optimization algorithm

COOA: Competitive optimization algorithm

A parallel grey wolf optimizer combined with opposition based learning

An Improved Model Of Brain Emotional Learning Algorithm Based On Interval Knowledge

Computational Model Of Social Intelligence Based On Emotional Learning In The Amygdala