Nature-Inspired Algorithms: State-of-Art, Problems and Prospects
20 Aug 2014-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 100, Iss: 14, pp 14-21
TL;DR: This study provides the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of required input parameters, their key evolutionary strategies and application areas to overcome the problem of ‘curse of dimensionality’.
Abstract: Nature-inspired algorithms have gained immense popularity in recent years to tackle hard real world (NP hard and NP complete) problems and solve complex optimization functions whose actual solution doesn’t exist. The paper presents a comprehensive review of 12 nature inspired algorithms. This study provides the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of required input parameters, their key evolutionary strategies and application areas. A list of automated toolboxes available for directly evaluating these nature inspired algorithms over numerical optimization problems indicates the need for unified toolbox for all nature inspired algorithms. It also elucidates the users with the minimum and maximum dimensions over which these algorithms have already been evaluated on benchmark test functions. Hence this study would aid the research community to know what all algorithms could be examined for large scale global optimization to overcome the problem of ‘curse of dimensionality’.
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TL;DR: The solution results quality of this study show that the proposed HFPSO algorithm provides fast and reliable optimization solutions and outperforms others in unimodal, simple multi-modal, hybrid, and composition categories of computationally expensive numerical functions.
292 citations
TL;DR: The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment.
Abstract: The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.
52 citations
TL;DR: An overview of multi-agent systems for microgrid control and management is presented, whereby various performance indicators and optimization algorithms are summarized and compared in terms of convergence time and performance in achieving system objectives and found that Particle Swarm Optimization has a good convergence time.
Abstract: Smart grids are considered a promising alternative to the existing power grid, combining intelligent energy management with green power generation. Decomposed further into microgrids, these small-scaled power systems increase control and management efficiency. With scattered renewable energy resources and loads, multi-agent systems are a viable tool for controlling and improving the operation of microgrids. They are autonomous systems, where agents interact together to optimize decisions and reach system objectives. This paper presents an overview of multi-agent systems for microgrid control and management. It discusses design elements and performance issues, whereby various performance indicators and optimization algorithms are summarized and compared in terms of convergence time and performance in achieving system objectives. It is found that Particle Swarm Optimization has a good convergence time, so it is combined with other algorithms to address optimization issues in microgrids. Further, information diffusion and consensus algorithms are explored, and according to the literature, many variants of average-consensus algorithm are used to asynchronously reach an equilibrium. Finally, multi-agent system for multi-microgrid service restoration is discussed. Throughout the paper, challenges and research gaps are highlighted in each section as an opportunity for future work.
32 citations
Cites background from "Nature-Inspired Algorithms: State-o..."
...The system is composed of ants, pheromone, daemon action and decentralized control, where the goal is to find paths leading to the “food” (Agarwal and Mehta 2014)....
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...…which randomly looks for new food sources; employed bee, which visits and exploits the found source; and on-looker bee, which waits on the dance area to make decisions on the food sources based on the information communicated by the employee bees (Mahalem and Chavan 2012; Agarwal and Mehta 2014)....
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...In this algorithm, three types of bees are defined: scout bee, which randomly looks for new food sources; employed bee, which visits and exploits the found source; and on-looker bee, which waits on the dance area to make decisions on the food sources based on the information communicated by the employee bees (Mahalem and Chavan 2012; Agarwal and Mehta 2014)....
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TL;DR: This paper select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization and sine cosine algorithm named as hybrid binary particle Swarm optimization and Sine cosines algorithm (HBPSOSCA).
Abstract: Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases.
31 citations
Cites methods from "Nature-Inspired Algorithms: State-o..."
...Nature inspired algorithms (NIA) gain attention for optimization problem (Agarwal and Mehta 2014)....
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TL;DR: KeywoRDS Ant Colony Optimization, Artificial Bee Colony, Cancer, Diabetes, Disease Diagnosis, Genetic Algorithm, Heart Disease, Nature Inspired Techniques, Particle Swarm Optimization.
Abstract: Genetic Algorithms GA, Ant Colony Optimization ACO, Particle Swarm Optimization PSO and Artificial Bee Colonies ABC are some vital nature inspired computing NIC techniques. These approaches have be...
30 citations
References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
01 Jan 1989
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.
33,034 citations
01 Jan 1989
12,457 citations
"Nature-Inspired Algorithms: State-o..." refers background or methods in this paper
...Genetic algorithm was given by John Holland during 1960s and 1970s [20] [21]....
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...Selection, Recombination and Mutation Machine Learning [21], Code Breaking, Computer automated design , Computer architecture, Bayesian inference [27], forensic science, Data Center/Server Farm, File allocation for a distributed system, game theory, robot behavior etc....
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01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
11,224 citations
"Nature-Inspired Algorithms: State-o..." refers methods in this paper
...In 1992, Marco Dorigo [24] proposed a new algorithm in his PhD thesis, Ant colony optimization....
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