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Current Trends and Ongoing Progress in the Computational Alignment of Biological Sequences

IEEE ACCESS 2019年 7卷 68380-68391页

作者： Ishaq, Muhammad Khan, Asfandyar Khan, Majid Imran, Muhammad Univ Agr Dept Comp & IT Peshawar 25003 Pakistan Univ Agr Dept Stat Math & Comp Sci Peshawar 25000 Pakistan

The computational techniques for nucleic acid and protein sequence comparison reduce the extensive burden of molecular biologists. The sequence alignment is one of the main research areas in bioinformatics, and comparative genomics and proteomics lead us to important discoveries in various fields of bioinformatics. Researchers develop and use different heuristics and evolutionary algorithms for optimal DNA and protein sequence alignment. There are different categories of improved computational sequence matching methods. In this paper, the goal is to cover almost all computational approaches toward sequence alignment. Different aspects and issues related to the optimal alignment of biological sequences will be analyzed. The sequence comparisons through mathematical and computational techniques have manifold benefits and importance in bioinformatics. Researchers recently explore proposing novel computational techniques for simultaneous matching of multiple sequences or multiple sequence alignment (MSA). Pairwise alignment, or the alignment of two sequences, is the basic building block of all alignment methods. The goal is to design optimal and relevant algorithms with less computational complexity and more efficiency.

关键词： Dynamic programming optimization multiple sequence alignment evolutionary computations categories of sequence alignment recent trends in sequence alignment local and global alignment pairwise alignment structure based alignment

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On operators and search space topology in multi-objective flow shop scheduling

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期181卷 195-206页

作者： Geiger, Martin Josef Univ Hohenheim Lehrstuhl Industriebetriebslehre D-70593 Stuttgart Germany

Multi-objective optimization using evolutionary algorithms identifies Pareto-optimal alternatives or their close approximation by means of a sequence of successive local improvement moves. While several successful applications to combinatorial optimization problems are known, studies of underlying problem structures are still scarce. The paper presents a study of the problem structure of multi-objective permutation flow shop scheduling problems and investigates the effectiveness of local search neighborhoods within an evolutionary search framework. First, small problem instances with up to six objective functions for which the optimal alternatives are known are studied. Second, benchmark instances taken from literature are investigated. It turns out for the investigated data sets that the Pareto-optimal alternatives are found relatively concentrated in alternative space. Also, it can be shown that no single neighborhood operator is able to equally identify all Pareto-optimal alternatives. Taking this into consideration, significant improvements have been obtained by combining different neighborhood structures into a multi-operator search framework. (c) 2006 Elsevier B.V. All rights reserved.

关键词： multiple objective programming flow shop scheduling local search evolutionary computations

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Multicriteria optimization of hard turning operation of the hardened 18HGT steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第1-4期49卷 305-312页

作者： Pytlak, Boguslaw Univ Bielsko Biala Dept Mfg Technol & Automat PL-43309 Bielsko Biala Poland

The research results on multicriteria optimization of hard finish turning operation of hardened 18HGT steel are presented. The steel was machined with the use of cubical boron nitride (CBN) inserts of Wiper geometry. The following cutting parameters were subjected to optimization: cutting speed v (c), feed f, and depth of cut a (p) . Unit production cost K (j) , time per unit t (j) , and resultant cutting force F were assumed as optimization criteria. The equations describing the effect of cutting parameters upon particular optimization criteria were elaborated on the basis of some empirical research. The multicriteria optimization was carried out with the application of traditional method of weights, as well as the modified distance method (MDM) based on evolutionary computations (EC). From the obtained optimal sets of Pareto solutions, the best solution was selected with the aid of the hierarchical optimization method.

关键词： Hard turning Cubical boron nitride Multicriteria optimization evolutionary computations

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Ant colony optimization for path planning in search and rescue operations

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第1期305卷 53-63页

作者： Morin, Michael Abi-Zeid, Irene Quimper, Claude-Guy Univ Laval Dept Operat & Decis Syst Quebec City PQ Canada Univ Laval Dept Comp Sci & Software Engn Quebec City PQ Canada

In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper, we address the problem of efficiently optimizing search paths in the context of the NP-hard optimal search path problem with visibility, based on search theory. To that end, we evaluate and develop ant colony optimization algorithm variants where the goal is to maximize the probability of finding a moving search object with Markovian motion, given a finite time horizon and finite resources (scans) to allocate to visible regions. Our empirical results, based on evaluating 96 variants of the metaheuristic with standard components tailored to the problem and using realistic size search environments, provide valuable insights regarding the best algorithm configurations. Furthermore, our best variants compare favorably, especially on the larger and more realistic instances, with a standard greedy heuristic and a state-of-the-art mixed-integer linear program solver. With this research, we add to the empirical body of evidence on an ant colony optimization algorithms configuration and applications, and pave the way to the implementation of search path optimization in operational decision support systems for search and rescue.(c) 2022 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Search and rescue Optimal search path planning Ant colony optimization Humanitarian operations

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Genetic algorithms in materials design and processing

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INTERNATIONAL MATERIALS REVIEWS 2004年第3-4期49卷 246-260页

作者： Chakraborti, N Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India

Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection. They are highly robust and efficient for most engineering optimising studies. Although a late entrant in the materials arena, GAs based studies are increasingly making their presence felt in many different aspects of this discipline. In recent times, GAs have been successfully used in numerous problems in the areas of atomistic material design, alloy design, polymer processing, powder compaction and sintering, ferrous production metallurgy, continuous casting, metal rolling, metal cutting, welding, and so on. The present review attempts to present the state of the art in this area. It includes three broad sections given as: fundamentals of genetic algorithms, genetic algorithms in materials design, and genetic algorithms in materials processing. The first section provides the reader with the basic concepts and the intricacies associated with this novel technique. The following section presents a detailed account of the usage of GAs to design various materials, predominantly at the atomic level. The third section aims to capture the process of applicability of GAs in numerous materials processing operations. A thorough literature search and critical analysis of the research conducted so far is provided, and attempts have been made to demonstrate how one single methodology can be utilised to study virtually every area of the vast materials discipline.

关键词： genetic algorithms evolutionary computations modelling simulation optimisation materials design materials processing

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Principles of scatter search

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2006年第2期169卷 359-372页

作者： Martí, R Laguna, M Glover, F Univ Valencia Fac Matemat Dept Estadistica & Invest Operat Valencia 46100 Spain Univ Colorado Leeds Sch Bus Boulder CO 80309 USA

Scatter search is an evolutionary method that has been successfully applied to hard optimization problems. The fundamental concepts and principles of the method were first proposed in the 1970s, based on formulations dating back to the 1960s for combining decision rules and problem constraints. In contrast to other evolutionary methods like genetic algorithms.. scatter search is founded on the premise that systematic designs and methods for creating new solutions afford significant benefits beyond those derived from recourse to randomization. It uses strategies for search diversification and intensification that have proved effective in a variety of optimization problems. This paper provides the main principles and ideas of scatter search and its generalized form path relinking. We first describe a basic design to give the reader the tools to create relatively simple implementations. More advanced designs derive from the fact that scatter search and path relinking are also intimately related to the tabu search (TS) metaheuristic, and gain additional advantage by making use of TS adaptive memory and associated memory-exploiting mechanisms capable of being tailored to particular contexts. These and other advanced processes described in the paper facilitate the creation of sophisticated implementations for hard problems that often arise in practical settings. Due to their flexibility and proven effectiveness, scatter search and path relinking can be successfully adapted to tackle optimization problems spanning a wide range of applications and a diverse collection of structures, as shown in the papers of this volume. Crown Copyright (c) 2004 Published by Elsevier B.V. All rights reserved.

关键词： metaheuristics evolutionary computations search theory path relinking

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Local dominance and local recombination in MOEAs on 0/1 multiobjective knapsack problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第3期181卷 1708-1723页

作者： Sato, Hiroyuki Aguirre, Hernan E. Tanaka, Kiyoshi Shinshu Univ Fac Engn Nagano 3808553 Japan

This work studies and compares the effects on performance of local dominance and local recombination applied with different locality in multiobjective evolutionary algorithms on combinatorial 0/1 multiobjective knapsack problems. For this purpose, we introduce a method that creates a neighborhood around each individual and assigns a local dominance rank after alignment of the principle search direction of the neighborhood by using polar coordinates in objective space. For recombination a different neighborhood determined around a random principle search direction is created. The neighborhood sizes for dominance and recombination are separately controlled by two different parameters. Experimental results show that the optimum locality of dominance is different from the optimum locality of recombination. Additionally, it is shown that the performance of the algorithm that applies local dominance and local recombination with different locality is significantly better than the performance of algorithms applying local dominance alone, local recombination alone, or dominance and recombination globally as conventional approaches do. (C) 2006 Elsevier B.V. All rights reserved.

关键词： evolutionary computations multiobjective combinatorial optimization 0/1 multiobjective knapsack problems local dominance local recombination alignment of the principle search direction polar coordinates optimum locality

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Countering the negative search bias of ant colony optimization in subset selection problems

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COMPUTERS & OPERATIONS RESEARCH 2013年第4期40卷 931-942页

作者： Verwaeren, Jan Scheerlinck, Karolien De Baets, Bernard Univ Ghent Dept Math Modelling Stat & Bioinformat KERMIT B-9000 Ghent Belgium

In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the Max-Min AS and applied to a real-life subset selection problem. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： evolutionary computations Combinatorial optimization Ant colony optimization Search bias Subset problems

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Expected runtimes of evolutionary algorithms for the Eulerian cycle problem

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COMPUTERS & OPERATIONS RESEARCH 2008年第9期35卷 2750-2759页

作者： Neumann, Frank Max Planck Inst Informat Dept Aglorithms & Complex 1 D-66123 Saarbrucken Germany

evolutionary algorithms are randomized search heuristics, which are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. They have successfully been applied to different kinds of arc routing problems. To start the analysis of evolutionary algorithms with respect to the expected optimization time on these problems, we consider the Eulerian cycle problem. We show that a variant of the well-known (1 + 1) EA working on the important encoding of permutations is able to find an Eulerian tour of an Eulerian graph in expected polynomial time. Altering the operator used for mutation in the considered algorithm, the expected optimization time changes from polynomial to exponential. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： evolutionary computations combinatorial optimization Eulerian cycles expected optimization time

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Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers

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IEEE TRANSACTIONS ON POWER DELIVERY 1999年第4期14卷 1342-1350页

作者： Yang, HT Liao, CC Chung Yuan Christian Univ Dept Elect Engn Chungli 320 Taiwan

To enhance the fault diagnosis abilities for the dissolved gas analysis (DGA) of the power transformers, this paper proposes a novel adaptive fuzzy system for the incipient fault recognition through evolution enhanced design approach. Complying with the practical gas records and associated fault causes as much as possible, a fuzzy reasoning algorithm is presented to establish a preliminary fuzzy diagnosis system. In the system, an evolutionary optimization algorithm is further relied on to fine-tune the membership functions of the if-then inference rules. To make the diagnosis system intensively compact and the inference process more understandable, a pruning scheme is then developed to filter out the insignificant or redundant rules. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test: data collected from Taiwan Power Company (TPC).

关键词： transformer dissolved gas analysis evolutionary computations fuzzy diagnosis system

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