Daniel Merkle

Bi-Criterion Optimization with Multi Colony Ant Algorithms

In this paper we propose a new approach to solve bi-criterion optimization problems with ant algorithms where several colonies of ants cooperate in finding good solutions. We introduce two methods for co-operation between the colonies and compare them with a multistart ant algorithm that corresponds to the case of no cooperation. Heterogeneous colonies are used in the algorithm, i.e. the ants differ in their preferences between the two criteria. Every colony uses two pheromone matrices -- each suitable for one optimization criterion. As a test problem we use the Single Machine Total Tardiness problem with changeover costs.

An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems

Ant Colony Optimization is an evolutionary method that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem. Compared to an existing ACO algorithm for the unweighted Total Tardiness Problem our algorithm has several improvements. The main novelty is that in our algorithm the ants are guided on their way to good solutions by sums of pheromone values. This allows the ants to take into account pheromone values that have already been used for making earlier decisions.

CREx: inferring genomic rearrangements based on common intervals

SUMMARY We present the web-based program CREx for heuristically determining pairwise rearrangement events in unichromosomal genomes. CREx considers transpositions, reverse transpositions, reversals and tandem-duplication-random-loss (TDRL) events. It supports the user in finding parsimonious rearrangement scenarios given a phylogenetic hypothesis. CREx is based on common intervals, which reflect genes that appear consecutively in several of the input gene orders. AVAILABILITY CREx is freely available at http://pacosy.informatik.uni-leipzig.de/crex

A parameter-adaptive dynamic programming approach for inferring cophylogenies

BackgroundCoevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phylogenies of both groups is often done by employing a set of possible types of events that happened during coevolution. Costs are assigned to the different types of events and a reconstruction of the common history with a minimal sum of event costs is sought.ResultsThis paper introduces a new algorithm and a corresponding tool called CoRe-PA, that can be used to infer the common history of coevolutionary systems. The proposed method utilizes an event-based concept for reconciliation analyses where the possible events are cospeciations, sortings, duplications, and (host) switches. All known event-based approaches so far assign costs to each type of cophylogenetic events in order to find a cost-minimal reconstruction. CoRe-PA uses a new parameter-adaptive approach, i.e., no costs have to be assigned to the coevolutionary events in advance. Several biological coevolutionary systems that have already been studied intensely in literature are used to show the performance of CoRe-PA.ConclusionFrom a biological point of view reasonable cost values for event-based reconciliations can often be estimated only very roughly. CoRe-PA is very useful when it is difficult or impossible to assign exact cost values to different types of coevolutionary events in advance.

Ant Colony Optimization with Global Pheromone Evaluation for Scheduling a Single Machine

Ant Colony Optimization (ACO) is a metaheuristic that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem and compare it to an existing ACO algorithm for the unweighted problem. The proposed algorithm has some novel properties that are of general interest for ACO optimization. A main novelty is that the ants are guided on their way through the decision space by global pheromone information instead of using only local pheromone information. It is also shown that the ACO optimization behaviour can be improved when priority scheduling heuristics are adapted so that they appropriately reflect absolute quality differences between the alternatives before they are used by the ants. Further improvements can be obtained by identifying situations where the ants can perform optimal decisions.

Modeling the dynamics of ant colony optimization

The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.

Reconstruction of the cophylogenetic history of related phylogenetic trees with divergence timing information

In this paper, we present a method and a corresponding tool called Tarzan for cophylogeny analysis of phylogenetic trees where the nodes are labelled with divergence timing information. The tool can be used for example to infer the common history of hosts and their parasites, of insect-plant relations or symbiotic relationships. Our method does the reconciliation analysis using an event-based concept where each event is assigned a cost and cost minimal solutions are sought. The events that are used by Tarzan are cospeciations, sortings, duplications, and (host) switches. Different from existing tools, Tarzan can handle more complex timing information of the phylogenetic trees for the analysis. This is important because several recent studies of cophylogenetic relationship have shown that timing information can be very important for the correct interpretation of results from cophylogenetic analysis. We present two examples (one host-parasite system and one insect-plant system) that show how divergence timing information can be integrated into reconciliation analysis and how this influences the results.

Evolution of Mitochondrial Gene Orders in Echinoderms

A comprehensive analysis of the mitochondrial gene orders of all previously published and two novel Antedon mediterranea (Crinoidea) and Ophiura albida (Ophiuroidea) complete echinoderm mitochondrial genomes shows that all major types of rearrangement operations are necessary to explain the evolution of mitochondrial genomes. In addition to protein coding genes we include all tRNA genes as well as the control region in our analysis. Surprisingly, 7 of the 16 genomes published in the GenBank database contain misannotations, mostly unannotated tRNAs and/or mistakes in the orientation of tRNAs, which we have corrected here. Although the gene orders of mt genomes appear very different, only 8 events are necessary to explain the evolutionary history of echinoderms with the exception of the ophiuroids. Only two of these rearrangements are inversions, while we identify three tandem-duplication-random-loss events and three transpositions.

Fast Ant Colony Optimization on Runtime Reconfigurable Processor Arrays

Ant Colony Optimization (ACO) is a metaheuristic used to solve combinatorial optimization problems. As with other metaheuristics, like evolutionary methods, ACO algorithms often show good optimization behavior but are slow when compared to classical heuristics. Hence, there is a need to find fast implementations for ACO algorithms. In order to allow a fast parallel implementation, we propose several changes to a standard form of ACO algorithms. The main new features are the non-generational approach and the use of a threshold based decision function for the ants. We show that the new algorithm has a good optimization behavior and also allows a fast implementation on reconfigurable processor arrays. This is the first implementation of the ACO approach on a reconfigurable architecture. The running time of the algorithm is quasi-linear in the problem size n and the number of ants on a reconfigurable mesh with n2 processors, each provided with only a constant number of memory words.

Dynamic Polyethism and Competition for Tasks in Threshold Reinforcement Models of Social Insects

In this paper we study the dynamics of task division in threshold reinforcement models of social insect societies. Our work extends other models in order to include several factors that influence the behavior of real insect colonies. Main extensions of our model are variable demands for work, age-dependent thresholds and finite life span of the individuals. It is shown how these factors influence the degree of task specialization of the individuals in a colony. Moreover, we show that the introduction of a threshold-dependent competition process between the individuals during task selection leads to the occurrence of specialists and differentiation between individuals as an emergent phenomenon that depends on the colony size. This result can help to explain the proximate mechanisms that lead to specialization in large insect colonies. Our results have implications for the fields of multi-agent systems, robotics, and nature inspired scheduling where threshold response models are used for control and regulation tasks.