(2012) Male-produced pheromone in the European woodwasp‚ Sirex noctilio. Journal of Chemical Ecology 38: 52–62. El-Sayed AM‚ Suckling DM‚ Wearing CH & Byers JA (2006) Potential of mass trapping for long-term pest management and eradication of invasive species. Journal of Economic
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vision and respiration in arthropods (Prawn‚ cockroach and scorpion); modification of mouth parts in insects (cockroach‚ mosquito‚ housefly‚ honey bee and butterfly); metamorphosis in insect and its hormonal regulation‚ social behaviour of Apis and termites. (i) Mollusca: Feeding‚ respiration‚ locomotion‚ general features and life history of Lamellidens‚ Pila and Sepia‚ torsion and detorsion in gastropods. (j) Echinodermata: Feeding‚ respiration‚ locomotion‚ larval forms‚ general features and life
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Example Take some dirt in your mouth Moisten it with pheromones Walk in the direction of the strongest pheromone concentration Drop what you are carrying where the smell is the strongest Ant Colony Optimization uses artificial stigmergy Swarm Intelligence Ant Colony Optimization Marco Dorigo (1991) –
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Table of contents Chapter No. 1. 2. 3. 4. Topics Introduction 1.1 Origin of Ant Colony Optimization 1.2 Towards Artificial Ants 1.3 ACO Metahueristic 1.4 Applying ACO to TSP 1.4.1 Detailed implementation of TSP with ACO 1.5 Ant System and Successors 1.5.1 Elitist Ant System 1.5.2 Rank Based Ant System 1.5.3 Max-Min Ant System 1.5.3 Ant Colony System Literature survey Further scope References 1 Page No. 4 5-7 7-9 9-10 10-11 11-14 14 14-15 15-16 16-18 18-19
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Insecticides 1. Jack R. Plimmer‚ 2. Derek W. Gammon Published Online: 16 APR 2004 DOI: 10.1002/0471238961.0914190513052003.a01.pub2 Copyright © 2001 by John Wiley & Sons‚ Inc. Book Title Kirk-Othmer Encyclopedia of Chemical Technology Abstract Insects constitute an immense drain on food resources worldwide‚ as well as being serious disease vectors. Insecticide applications are made directly to raw agricultural commodities to protect plants and animals from insect attacks. Official national and
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CHAPTER 2 Cuckoo Search Algorithm This chapter provides a breif introduction to the area of Nature Inspired Algorithms and its classification. Among all the categories‚ Swarm intelligence will be the main area of focus. Various swarm intelligence techinques will be discussed in the later sections. Cuckoo Search Algorithm is also one of the latest swarm intelligence technique which is focused in this dissertation to solve the problem discussed in chapter 1. 1. Nature Inspired Algorithms Nature has
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violations and generates the timetable. In the experiment‚ the most influence factor is the heuristic information (β) and the other influence factors are pheromone evaporation rate (ρ)‚ pheromone weight (α) and no of ants (A) and no of iterations (I). Figure 5.1: Parameters used in the different size of UCTP Parameter Small Medium Large Pheromone weight (α) 1 1
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Overview of Algorithms for Swarm Intelligence Shu-Chuan Chu1‚ Hsiang-Cheh Huang2‚ John F. Roddick1‚ and Jeng-Shyang Pan3 1 School of Computer Science‚ Engineering and Mathematics‚ Flinders University of South Australia‚ Australia 2 National University of Kaohsiung‚ 700 University Road‚ Kaohsiung 811‚ Taiwan‚ R.O.C. 3 National Kaohsiung University of Applied Sciences‚ 415 Chien-Kung Road‚ Kaohsiung 807‚ Taiwan‚ R.O.C. Abstract. Swarm intelligence (SI) is based on collective behavior
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ACS terbukti merupakan metodologi yang paling optimal dalam menemukan jalur terpendek. Dan penelitian ini telah berhasil membuktikan keoptimalan dari ACS dalam menemukan solusi terhadap TSP. Keyword: Genetic Algorithm‚ Ant System‚ Software Agent‚ Pheromone‚ state transition rule 1. Pengantar Salah rekayasa satu paradigma lunak baru dalam dapat dihindari akan dibutuhkannya lebih dari satu agent‚ seiring dengan semakin kompleksnya tugas yang dikerjakan oleh sistem tersebut. Paradigma pengembangan sistem
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1988 by the “Dalle Molle” Foundation which promoted quality of life. Contents 1 Introduction 2 Routing in mobile ad hoc networks 3 Ant Colony Optimization for routing: general principles 4 The 4.1 4.2 4.3 4.4 4.5 AntHocNet routing algorithm Pheromone tables . . . . . . . . . . . . . . . . . Reactive route setup . . . . . . . . . . . . . . . Proactive route maintenance and improvement Data forwarding . . . . . . . . . . . . . . . . . Dealing with link failures . . . . . . . . . . . . . . . .
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