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Exploring Ant Colony Optimisation for Adaptive Interactive Search [chapter]

M-Dyaa Albakour, Udo Kruschwitz, Nikolaos Nanas, Dawei Song, Maria Fasli, Anne De Roeck
2011 Lecture Notes in Computer Science  
We explore a biologically inspired model based on ant colony optimisation applied to query logs as an adaptive learning process that addresses the problem of deriving query suggestions.  ...  In this paper we use a novel automatic evaluation framework based on actual query logs to explore the effect of different parameters in the ant colony optimisation algorithm on the performance of the resulting  ...  In this paper we explored variations of the ant colony optimisation algorithm by conducting controlled, deterministic and fully reproducible experiments.  ... 
doi:10.1007/978-3-642-23318-0_20 fatcat:hi7lypy4w5eorm5bcuw4vddu24

Interactive preferences in multiobjective ant colony optimisation for assembly line balancing

Manuel Chica, Óscar Cordón, Sergio Damas, Joaquín Bautista
2014 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
By solving the problem in these scenarios, we show the search guidance advantages of Communicated by V. Loia. using an interactive multiobjective ant colony optimisation method.  ...  In this contribution, we propose an interactive multicriteria optimisation framework for the time and space assembly line balancing problem.  ...  In conjunction with g-dominance, we use an existing memetic multiobjective ant colony optimisation (MOACO) approach for the TSALBP.  ... 
doi:10.1007/s00500-014-1451-1 fatcat:6gejvdnojrd2ha4w2udjekrpra

A Review: On using ACO Based Hybrid Algorithms for Path Planning of Multi-Mobile Robotics

Ibrahim Ismael Hamad, Mohammad S. Hasan
2020 International Journal of Interactive Mobile Technologies  
This paper reviews articles on Ant Colony Optimisation (ACO) and its hybrid versions to solve the problem.  ...  <p class="0abstract"><strong>Abstract-</strong>The path planning for Multi Mobile Robotic (MMR) system is a recent combinatorial optimisation problem.  ...  Ant Colony Optimisation (ACO) According to the natural ant characteristics, initially individual ants in a colony come out of the nest and depart in different directions searching for food randomly.  ... 
doi:10.3991/ijim.v14i18.16371 fatcat:ufakgwcsezdexhnuclcx42seey

Dynamic Ant Colony Optimisation

Daniel Angus, Tim Hendtlass
2005 Applied intelligence (Boston)  
This paper explores the ability of the ant colony optimisation algorithm to adapt from the optimum solution to one set of circumstances to the optimal solution to another set of circumstances.  ...  Ant Colony optimisation has proved suitable to solve static optimisation problems, that is problems that do not change with time.  ...  Fig. 2 . 2 The shortest path for the Burma 14 data set. Fig. 3 . 3 The path lengths found using ant colony optimisation.  ... 
doi:10.1007/s10489-005-2370-8 fatcat:fayjmv3cofhr3kifo3ivc5patq

Metaheuristics in nature-inspired algorithms

Michael A. Lones
2014 Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion - GECCO Comp '14  
any fundamental new knowledge to the field of metaheuristics, and suggest some guidelines for future research in this field.  ...  To address this, this paper attempts to explicitly identify the metaheuristics that are used in conventional optimisation algorithms, discuss whether more recent nature-inspired algorithms have delivered  ...  ANT COLONY OPTIMISATION Ant colony optimisation (ACO) [1] is motivated by the way in which ants share their foraging experience.  ... 
doi:10.1145/2598394.2609841 dblp:conf/gecco/Lones14 fatcat:qnksueu37bd4xllp3vtdesoq4a

New inspirations in swarm intelligence: a survey

R.S. Parpinelli, H.S. Lopes
2011 International Journal of Bio-Inspired Computation (IJBIC)  
The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods.  ...  This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics.  ...  Parpinelli; as well as to the Brazilian National Research Council (CNPq) for the Research Grant No. 309262/2007-0 to H.S. Lopes.  ... 
doi:10.1504/ijbic.2011.038700 fatcat:4ww2nroqtfebzffylcdwitjwyi

Self-adaptive ant colony optimisation applied to function allocation in vehicle networks

Manuel Förster, Bettina Bickel, Bernd Hardung, Gabriella Kókai
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
Since it concerns a multi-objective optimisation problem, multiple ant colonies are employed.  ...  To reduce the effort needed to adapt the algorithm to the optimisation problem by tuning strategic parameters, self-adaptive mechanisms are established for most of them.  ...  This way, the following ants will less likely follow the same path and the probability for exploring new regions of the search space is higher.  ... 
doi:10.1145/1276958.1277352 dblp:conf/gecco/ForsterBHK07 fatcat:542uiompvrg3po7sd3yv46uhyi

Anti-pheromone as a Tool for Better Exploration of Search Space [chapter]

James Montgomery, Marcus Randall
2002 Lecture Notes in Computer Science  
Ant algorithms for multi-objective optimisation and those employing multiple colonies have made use of more than one pheromone, but the interactions between these different pheromones are largely simple  ...  The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation.  ...  Hence, instead of optimising across two objective functions, PAP allows some ants to explore apparently poorer areas of the search space while other ants focus on the solution space near the current global  ... 
doi:10.1007/3-540-45724-0_9 fatcat:wetw7wzoqjfxrkhidgv4qq7aau

New progresses in swarm intelligence-based computation

Haibin Duan, Qinan Luo
2015 International Journal of Bio-Inspired Computation (IJBIC)  
This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm,  ...  Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence.  ...  The SI-based searching mechanism with an effective population initialisation and an adaptive search strategy is helpful to perform exploration for promising solutions (Pehlivanoglu, 2013) .  ... 
doi:10.1504/ijbic.2015.067981 fatcat:a6naj3jjmrepvbdfk5tuzcltqe

Validation of Hybridized Particle Swarm Optimization (PSO) Algorithm with the Pheromone Mechanism of Ant Colony Optimization (ACO) using Standard Benchmark Function

Mogaji Stephen, Alese Boniface, Adetunmbi Adebayo
2018 Communications on Applied Electronics  
the correctness and convergence of the Hybridized PSO optimization mode for minimization.  ...  of "intelligent" global behavior, unknown to the individual agents This research work aims at hybridizing the conventional Particle Swarm Optimization (PSO) algorithm with the pheromone mechanism of Ant  ...  SYSTEM ANALYSIS AND DESIGN 4.1 Hybridization of Particle Swarm Optimisation using Ant Colony Optimisation Swarm intelligence meta-heuristics, namely, particle swarm optimisation and ant colony optimisation  ... 
doi:10.5120/cae2018652799 fatcat:kwunshv26fby7iqcaxogdqkw2e

Exploiting Click Logs for Adaptive Intranet Navigation [chapter]

Sharhida Zawani Saad, Udo Kruschwitz
2013 Lecture Notes in Computer Science  
We find that users managed to conduct the tasks significantly quicker than the (purely frequencybased) baseline by employing ant colony optimisation or random walk approaches to the log data for building  ...  In this paper we explore three different algorithms that can be employed to learn such suggestions from navigation logs.  ...  In our case the input for building the model are the clicks recorded for URLs within a session. -Ant Colony Optimisation Model (System C) is another adaptive learning approach.  ... 
doi:10.1007/978-3-642-36973-5_86 fatcat:o5zsjtwth5cnzi6gqbdzidiani

Epigenetic opportunities for Evolutionary Computation [article]

Sizhe Yuen, Thomas H.G. Ezard, Adam J. Sobey
2021 arXiv   pre-print
Evolutionary Computation is a group of biologically inspired algorithms used to solve complex optimisation problems.  ...  This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within Evolutionary Computation.  ...  Acknowledgement The authors would like to thank the Lloyds Register Foundation and the Southampton Marine and Maritime Institute for their kind support of this research.  ... 
arXiv:2108.04546v1 fatcat:snpq5ydo2bg7zi3m3egibg7fja

Improving exploration in Ant Colony Optimisation with antennation

Christopher Beer, Tim Hendtlass, James Montgomery
2012 2012 IEEE Congress on Evolutionary Computation  
Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic).  ...  INTRODUCTION Ant Colony Optimisation (ACO) algorithms, modeled on the behavior of ant colonies, are a class of nature-inspired meta-heuristic search techniques that use a combination of heuristics to influence  ...  Ant Colony Systems The Ant Colony System [5] was developed to compensate for problems with the AS algorithm when solving large TSPs.  ... 
doi:10.1109/cec.2012.6252923 dblp:conf/cec/BeerHM12 fatcat:mxzqxoyff5b35p7yvnjykx3qoa

An intelligent methodology for optimising machining operation sequence by ant system algorithm

Sneha Singh, Sankha Deb
2014 International Journal of Industrial and Systems Engineering  
A comparative study shows that for a demonstration run, the proposed ant system-based approach performed faster than previously developed methodologies for ant colony optimisation as well as a genetic  ...  algorithm-based optimisation techniques.  ...  Acknowledgements The authors would like to sincerely thank the reviewers for their constructive and helpful recommendations to revise this paper.  ... 
doi:10.1504/ijise.2014.060654 fatcat:ae7dlvpcmveklp5up72bz5ve6y

Synthetic Genes for Artificial Ants. Diversity in Ant Colony Optimization Algorithms

Sorin C. Negulescu, Ioan Dzitac, Alina E. Lascu
2010 International Journal of Computers Communications & Control  
ant colony optimisation (ACO) algorithm that incorporates methods and ideas from genetic algorithms (GA).  ...  Inspired from the fact that the real world ants from within a colony are not clones (although they may look alike, they are different from one another), in this paper, the authors are presenting an adapted  ...  Lascu Synthetic Genes for Artificial Ants. Diversity in Ant Colony Optimization Algorithms  ... 
doi:10.15837/ijccc.2010.2.2476 fatcat:3p4suwfig5ckxikqlgneg2wsai
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