578 research outputs found
Solving multiple sequence alignment problems by using a swarm intelligent optimization based approach
In this article, the alignment of multiple sequences is examined through swarm intelligence based an improved particle swarm optimization (PSO). A random heuristic technique for solving discrete optimization problems and realistic estimation was recently discovered in PSO. The PSO approach is a nature-inspired technique based on intelligence and swarm movement. Thus, each solution is encoded as “chromosomes” in the genetic algorithm (GA). Based on the optimization of the objective function, the fitness function is designed to maximize the suitable components of the sequence and reduce the unsuitable components of the sequence. The availability of a public benchmark data set such as the Bali base is seen as an assessment of the proposed system performance, with the potential for PSO to reveal problems in adapting to better performance. This proposed system is compared with few existing approaches such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) alignment (DIALIGN), PILEUP8, hidden Markov model training (HMMT), rubber band technique-genetic algorithm (RBT-GA) and ML-PIMA. In many cases, the experimental results are well implemented in the proposed system compared to other existing approaches
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
Economic Load Dispatch depicts a fundamental role in the operation of power
systems, as it decreases the environmental load, minimizes the operating cost,
and preserves energy resources. The optimal solution to Economic Load Dispatch
problems and various constraints can be obtained by evolving several
evolutionary and swarm-based algorithms. The major drawback to swarm-based
algorithms is premature convergence towards an optimal solution. Fitness
Dependent Optimizer is a novel optimization algorithm stimulated by the
decision-making and reproductive process of bee swarming. Fitness Dependent
Optimizer (FDO) examines the search spaces based on the searching approach of
Particle Swarm Optimization. To calculate the pace, the fitness function is
utilized to generate weights that direct the search agents in the phases of
exploitation and exploration. In this research, the authors have carried out
Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by
reducing fuel cost, emission allocation, and transmission loss. Moreover, the
authors have enhanced a novel variant of Fitness Dependent Optimizer, which
incorporates novel population initialization techniques and dynamically
employed sine maps to select the weight factor for Fitness Dependent Optimizer.
The enhanced population initialization approach incorporates a quasi-random
Sabol sequence to generate the initial solution in the multi-dimensional search
space. A standard 24-unit system is employed for experimental evaluation with
different power demands. Empirical results obtained using the enhanced variant
of the Fitness Dependent Optimizer demonstrate superior performance in terms of
low transmission loss, low fuel cost, and low emission allocation compared to
the conventional Fitness Dependent Optimizer. The experimental study obtained
7.94E-12.Comment: 42 page
Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy
The particle swarm system simulates the evolution of the social mechanism. In this system, the individual particle representing the potential solution flies in the multidimensional space in order to find the better or the optimal solution. But because of the search path and limited speed, it's hard to avoid local best and the premature phenomenon occurs easily. Based on the uncertain principle of the quantum mechanics, the global search ability of the quantum particle swarm optimization (QPSO) algorithms are better than the particle swarm optimization algorithm (PSO). On the basis of the fundamental quantum PSO algorithm, this article introduces the grouping optimization strategy, and meanwhile adopts the dynamic adjustment, quantum mutation and possibility acceptance criteria to improve the global search capability of the algorithm and avoid premature convergence phenomenon. By optimizing the test functions, the experimental simulation shows that the proposed algorithm has better global convergence and search ability
Multiple sequence alignment using particle swarm optimization
The recent advent of bioinformatics has given rise to the central and recurrent problem of optimally aligning biological sequences. Many techniques have been proposed in an attempt to solve this complex problem with varying degrees of success. This thesis investigates the application of a computational intelligence technique known as particle swarm optimization (PSO) to the multiple sequence alignment (MSA) problem. Firstly, the performance of the standard PSO (S-PSO) and its characteristics are fully analyzed. Secondly, a scalability study is conducted that aims at expanding the S-PSO’s application to complex MSAs, as well as studying the behaviour of three other kinds of PSOs on the same problems. Experimental results show that the PSO is efficient in solving the MSA problem and compares positively with well-known CLUSTAL X and T-COFFEE.Dissertation (MSc)--University of Pretoria, 2009.Computer ScienceUnrestricte
Biochemical systems identification by a random drift particle swarm optimization approach
BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study
Intelligent Business Process Optimization for the Service Industry
The company\u27s sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Intelligent Business Process Optimization for the Service Industry
The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization
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