A multiobjective swarm intelligence approach based on artificial bee colony for reliable DNA sequence design

The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published.     

A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.     

Asymptotic convergence of metaheuristics for multiobjective optimization problems

This paper analyzes the convergence of metaheuristics used for multiobjective optimization problems in which the transition probabilities use a uniform mutation rule. We prove that these algorithms converge only if elitism is used.     

Comparing GPU-parallelized metaheuristics to branch-and-bound for batch plants optimization

The Journal of Supercomputing - We systematically compare two approaches with the optimal design of multiproduct batch plants that are widely used, e.g., in the chemical industry. Deterministic...     

Cooperative particle swarm optimization for multiobjective transportation planning

The paper presents a multiobjective optimization problem that considers distributing multiple kinds of products from multiple sources to multiple targets. The problem is of high complexity and is difficult to solve using classical heuristics. We propose for the problem a hierarchical cooperative optimization approach that decomposes the problem into low-dimensional subcomponents, and applies Pareto-based particle swarm optimization (PSO) method to the main problem and the subproblems alternately. In particular, our approach uses multiple sub-swarms to evolve the sub-solutions concurrently, controls the detrimental effect of variable correlation by reducing the subproblem objectives, and brings together the results of the sub-swarms to construct effective solutions of the original problem. Computational experiment demonstrates that the proposed algorithm is robust and scalable, and outperforms some state-of-the-art constrained multiobjective optimization algorithms on a set of test problems.     

The crowd framework for multiobjective particle swarm optimization

Multiobjective particle swarm optimization meets two difficulties—guiding the search towards the Pareto front and maintaining diversity of the obtained solutions—so a great number of improvements are possible. Our crowd framework systematically summarizes these improvements, extracts them into reusable strategies and categorizes them into modules by their optimization mechanisms. We introduce a number of new techniques within the modules. Strategies are compared first theoretically and then practically through amended ZDT series. We propose a sequence for module application based on the correlation between the modules. The resulting algorithms give incredible performance. Thus our crowd framework forms a new baseline for MOPSO.     

Comparing descent heuristics and metaheuristics for the vehicle routing problem

Three improvement heuristics for the vehicle routing problem are considered: a descent heuristic and two metaheuristics Simulated Annealing and Tabu Search. In order to make an in-depth comparison of the performance of these improvement heuristics, their behavior is analyzed on a heuristic, time-sensitive level as well as on a parametric level. The design and the results of the experiments are outlined. The external validity of the conclusions is discussed.Scope and purposeTabu Search (TS) and Simulated Annealing (SA) have demonstrated to be appropriate metaheuristics for solving NP-hard combinatorial optimization problems, such as the vehicle routing problem with side-constraints. In order to compare the performances of both metaheuristics with each other and with a traditional descent implementation, a comparison of the best solution independent of computing times is fundamentally wrong because metaheuristics have no unambiguous stopping criteria, as opposed to traditional descent implementations.     

Tuning metaheuristics: A data mining based approach for particle swarm optimization

The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic’s efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models.     

Solving multiobjective problems using cat swarm optimization

This paper proposes a new multiobjective evolutionary algorithm (MOEA) by extending the existing cat swarm optimization (CSO). It finds the nondominated solutions along the search process using the concept of Pareto dominance and uses an external archive for storing them. The performance of our proposed approach is demonstrated using standard test functions. A quantitative assessment of the proposed approach and the sensitivity test of different parameters is carried out using several performance metrics. The simulation results reveal that the proposed approach can be a better candidate for solving multiobjective problems (MOPs).     

Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems

This article presents an approach to integrate a Pareto dominance concept into a comprehensive learning particle swarm optimizer (CLPSO) to handle multiple objective optimization problems. The multiobjective comprehensive learning particle swarm optimizer (MOCLPSO) also integrates an external archive technique. Simulation results (obtained using the codes made available on the Web at http://www.ntu.edu.sg/home/EPNSugan) on six test problems show that the proposed MOCLPSO, for most problems, is able to find a much better spread of solutions and faster convergence to the true Pareto‐optimal front compared to two other multiobjective optimization evolutionary algorithms. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 209–226, 2006.     

Novel information discovery for intelligence and counterterrorism

Intelligence analysts construct hypotheses from large volumes of data, but are often limited by social and organizational norms and their own preconceptions and biases. The use of exploratory data mining technology can mitigate these limitations by requiring fewer assumptions. We present the design of the ATHENS system, which discovers novel information, relative to a specified set of existing knowledge, in large information repositories such as the World Wide Web. We illustrate the use of the system by starting from the terms “al Qaeda” and “bin Laden”" and running the ATHENS system as if on September 12th, 2001. This provides a picture of what novel information could have been known at the time. This is of some intrinsic interest, but also serves to validate the performance of the system since much of this novel information has been discovered by conventional means in the intervening years.     

Constrained multilinear detection for faster functional motif discovery

The Graph Motif problem asks whether a given multiset of colors appears on a connected subgraph of a vertex-colored graph. The fastest known parameterized algorithm for this problem is based on a reduction to the k-Multilinear Detection (k-MlD) problem: the detection of multilinear terms of total degree k in polynomials presented as circuits. We revisit k-MlD and define k-CMlD, a constrained version of it which reflects Graph Motif more faithfully. We then give a fast algorithm for k-CMlD. As a result we obtain faster parameterized algorithms for Graph Motif and variants of it.     

A disk-aware algorithm for time series motif discovery

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.     

Editorial survey: swarm intelligence for data mining

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.     

Assuming multiobjective metaheuristics to solve a three-objective optimisation problem for Relay Node deployment in Wireless Sensor Networks

This paper deals with how to efficiently deploy energy-harvesting Relay Nodes in previously established low-cost static Wireless Sensor Networks (WSNs), assuming a single-tiered network model. The purpose is to optimise three conflicting objectives: Average Energy Cost, Average Sensitivity Area, and Network Reliability. This is the so-called Relay Node Placement Problem (RNPP), which is an NP-hard optimisation problem. We find many works assuming heuristics in the current literature. However, it is not the case for metaheuristics, which usually provide good results solving such complex problems. This situation led us to consider a wide range of MultiObjective (MO) metaheuristics: the two standard Genetic Algorithms NSGA-II and SPEA2, the trajectory algorithm MO-VNS, the algorithm based on decomposition MOEA/D, and two novel swarm intelligence algorithms MO-ABC and MO-FA, which are based on the behaviour of honey bees and fireflies, respectively. These metaheuristics are applied to optimise a freely available data set. The results obtained are analysed considering two MO metrics: hypervolume and set coverage. Through a widely accepted statistical methodology, we conclude that MO-FA provides the best performance on average. We also study the efficiency of this approach, verifying that it is a good strategy to optimise such networks, including some limitations. Finally, we compare this proposal to another author approach, which assumes a heuristic.     

Coordinating metaheuristic agents with swarm intelligence

Coordination of multi agent systems remains as a problem since there is no prominent method suggests any universal solution. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems using metaheuristic algorithms. An idea for coordinating metaheuristic agents borrowed from swarm intelligence is introduced in this paper. This swarm intelligence-based coordination framework has been implemented as swarms of simulated annealing agents collaborated with particle swarm optimization for multidimensional knapsack problem. A comparative performance analysis is also reported highlighting that the implementation has produced much better results than the previous works.