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1.Emerging research on swarm intelligence and algorithm optimization

Author:Shi, Yuhui

Source:Emerging Research on Swarm Intelligence and Algorithm Optimization,2014,Vol.

Abstract:Throughout time, scientists have looked to nature in order to understand and model solutions for complex real-world problems. In particular, the study of self-organizing entities, such as social insect populations, presents a new opportunity within the field of artificial intelligence. Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior of decentralized systems in the natural world can be applied to intelligent system design. Discussing the application of swarm principles, optimization techniques, and key algorithms being used in the field, this publication serves as an essential reference for academicians, upper-level students, IT developers, and IT theorists. © 2015 by IGI Global. All rights reserved.

2.Bio-Inspired Computation and Optimization: An Overview

Author:Yang,Xin She;Chien,Su Fong;Ting,Tiew On

Source:Bio-Inspired Computation in Telecommunications,2015,Vol.

Abstract:All design problems in telecommunications can be formulated as optimization problems, and thus may be tackled by some optimization techniques. However, these problems can be extremely challenging due to the stringent time requirements, complex constraints, and a high number of design parameters. Solution methods tend to use conventional methods such as Lagrangian duality and fractional programming in combination with numerical solvers, while new trends tend to use evolutionary algorithms and swarm intelligence. This chapter provides a summary review of the bio-inspired optimization algorithms and their applications in telecommunications. We also discuss key issues in optimization and some active topics for further research.

3.Population diversity of particle swarm optimizer solving single-and multi-objective problems

Author:Cheng, Shi ; Shi, Yuhui ; Qin, Quande

Source:Emerging Research on Swarm Intelligence and Algorithm Optimization,2014,Vol.

Abstract:Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities exploration of new possibilities and exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained. Another issue in multiobjective is the solution metric. Pareto domination is utilized to compare two solutions; however, solutions are almost Pareto non-dominated for multiobjective problems with more than ten objectives. In this chapter, the authors analyze the population diversity of a particle swarm optimizer for solving both single objective and multiobjective problems. The population diversity of solutions is used to measure the goodness of a set of solutions. This metric may guide the search in problems with numerous objectives. Adaptive optimization algorithms can be designed through controlling the balance between exploration and exploitation. © 2015 by IGI Global. All rights reserved.

4.Bio-Inspired Computation in Telecommunications

Author:Yang,Xin She;Chien,Su Fong;Ting,Tiew On

Source:Bio-Inspired Computation in Telecommunications,2015,Vol.

Abstract:Bio-inspired computation, especially those based on swarm intelligence, has become increasingly popular in the last decade. Bio-Inspired Computation in Telecommunications reviews the latest developments in bio-inspired computation from both theory and application as they relate to telecommunications and image processing, providing a complete resource that analyzes and discusses the latest and future trends in research directions. Written by recognized experts, this is a must-have guide for researchers, telecommunication engineers, computer scientists and PhD students.

5.Developmental swarm intelligence Developmental learning perspective of swarm intelligence algorithms

Author:Shi, Yuhui

Source:Nature-Inspired Computing Concepts, Methodologies, Tools, and Applications,2016,Vol.1-3

Abstract:In this article, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm. © 2017 by IGI Global. All rights reserved.

6.Experimental study on boundary constraints handling in particle swarm optimization from a population diversity perspective

Author:Cheng, Shi ; Shi, Yuhui ; Qin, Quande

Source:Emerging Research on Swarm Intelligence and Algorithm Optimization,2014,Vol.

Abstract:Premature convergence happens in Particle Swarm Optimization (PSO) for solving both multimodal problems and unimodal problems. With an improper boundary constraints handling method, particles may get "stuck in" the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity is an effective way to monitor an algorithm's ability of exploration and exploitation. Through the population diversity measurement, useful search information can be obtained. PSO with a different topology structure and a different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this chapter, the phenomenon of particles getting "stuck in" the boundary in PSO is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques, and analyze the impact of these settings on the algorithm's abilities of exploration and exploitation. From these experimental studies, an algorithm's abilities of exploration and exploitation can be observed and the search information obtained; therefore, more effective algorithms can be designed to solve problems. © 2015 by IGI Global. All rights reserved.

7.Analytics on fireworks algorithm solving problems with shifts in the decision space and objective space

Author:Cheng, Shi ; Chen, Junfeng ; Qin, Quande ; Shi, Yuhui ; Zhang, Qingyu

Source:Nature-Inspired Computing Concepts, Methodologies, Tools, and Applications,2016,Vol.2-3

Abstract:Fireworks algorithms for solving problems with the optima shift in decision space and/or objective space are analyzed in this paper. The standard benchmark problems have several weaknesses in the research of swarm intelligence algorithms for solving single objective problems. The optimum is in the center of search range, and is the same at each dimension of the search space. The optimum shift in decision space and/or objective space could increase the difficulty of problem solving. A mapping strategy, modular arithmetic mapping, is utilized in the original fireworks algorithm to handle solutions out of search range. The solutions are implicitly guided to the center of search range for problems with symmetrical search range via this strategy. The optimization performance of fireworks algorithm on shift functions may be affected by this strategy. Four kinds of mapping strategies, which include mapping by modular arithmetic, mapping to the boundary, mapping to stochastic region, and mapping to limited stochastic region, are compared on problems with different dimensions and different optimum shift range. From experimental results, the fireworks algorithms with mapping to the boundary, or mapping to limited stochastic region obtain good performance on problems with the optimum shift. This is probably because the search tendency is kept in these two strategies. The definition of population diversity measurement is also proposed in this paper, from observation on population diversity changes, the useful information of fireworks algorithm solving different kinds of problems could be obtained. © 2017 by IGI Global. All rights reserved.

8.Hybrid metaheuristic algorithms: Past, present, and future

Author:Ting,T. O.;Yang,Xin She;Cheng,Shi;Huang,Kaizhu

Source:Studies in Computational Intelligence,2015,Vol.585

Abstract:Hybrid algorithms play a prominent role in improving the search capability of algorithms. Hybridization aims to combine the advantages of each algorithm to form a hybrid algorithm, while simultaneously trying to minimize any substantial disadvantage. In general, the outcome of hybridization can usually make some improvements in terms of either computational speed or accuracy. This chapter surveys recent advances in the area of hybridizing different algorithms. Based on this survey, some crucial recommendations are suggested for further development of hybrid algorithms.

9.Optimization of drilling process via weightless swarm algorithm

Author:Ting,T. O.

Source:Emerging Research on Swarm Intelligence and Algorithm Optimization,2014,Vol.

Abstract:In this chapter, the main objective of maximizing the Material Reduction Rate (MRR) in the drilling process is carried out. The model describing the drilling process is adopted from the authors' previous work. With the model in hand, a novel algorithm known as Weightless Swarm Algorithm is employed to solve the maximization of MRR due to some constraints. Results show that WSA can find solutions effectively. Constraints are handled effectively, and no violations occur; results obtained are feasible and valid. Results are then compared to previous results by Particle Swarm Optimization (PSO) algorithm. From this comparison, it is quite impossible to conclude which algorithm has a better performance. However, in general, WSA is more stable compared to PSO, from lower standard deviations in most of the cases tested. In addition, the simplicity of WSA offers abundant advantages as the presence of a sole parameter enables easy parameter tuning and thereby enables this algorithm to perform to its fullest.

10.Bio-Inspired Approaches in Telecommunications

Author:Chien,Su Fong;Zarakovitis,C. C.;Ting,Tiew On;Yang,Xin She

Source:Bio-Inspired Computation in Telecommunications,2015,Vol.

Abstract:Bio-inspired algorithms are modern optimization tools that are capable of solving complex design problems in many applications. Such algorithms aim to speed up the optimization process so as to tackle tougher optimization problems. Some of these algorithms, such as particle swarm optimization and cuckoo search, have been found to be much more feasible and practical in obtaining the optimal solution, compared to conventional mathematical methods. In this chapter, we will review design problems and their solution methods concerning resource and power allocations in orthogonal frequency division multiple access systems.

11.An optimization algorithm based on brainstorming process

Author:Shi, Yuhui

Source:Emerging Research on Swarm Intelligence and Algorithm Optimization,2014,Vol.

Abstract:In this chapter, the human brainstorming process is modeled, based on which two versions of a Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc, which points at one direction for future research on BSO algorithms. © 2015 by IGI Global. All rights reserved.
Total 11 results found
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