For Sonar Automatic Target Recognition problems, the number of mine-like objects is relatively small compared to the number non-mine-like objects available. This creates a heavy bias towards non-mine-like objects and ...
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ISBN:
(数字)9781510608665
ISBN:
(纸本)9781510608665
For Sonar Automatic Target Recognition problems, the number of mine-like objects is relatively small compared to the number non-mine-like objects available. This creates a heavy bias towards non-mine-like objects and increases the processing resources needed for classifier training. In order to reduce resource needs and the bias towards non-mine-like objects, we investigate selection methods for reducing the non-mine-like target samples while still maintaining as much of the original training information as possible. Specifically, we investigate methods for reducing sample size and bias while maintaining good classifier performance. Several methods are considered during this investigation that cover a wide range of techniques, including clustering and evolutionary algorithms. Each method is evaluated based on the classifier performance when trained on the chosen data samples and the execution time to select the new training set. Results on each method tested are presented using sonar data collected using a sidescan sonar system.
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of ...
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ISBN:
(纸本)9781509047802
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.
Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interact...
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ISBN:
(数字)9783319635378
ISBN:
(纸本)9783319635361;9783319635378
Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interactions between robots and animals. Behavioural zebrafish experiments show that their individual behaviours depend on social interactions producing collective behaviour and depend on their position in the environment. Based on those observations we build a multilevel model to describe the zebrafish collective behaviours in a structured environment. Here, we present this new model segmented in spatial zones that each corresponds to different behavioural patterns. We automatically fit the model parameters for each zone to experimental data using a multi-objective evolutionary algorithm. We then evaluate how the resulting calibrated model compares to the experimental data. The model is used to drive the behaviour of a robot that has to integrate socially in a group of zebrafish. We show experimentally that a biomimetic multilevel and context-dependent model allows good social integration of fish and robots in a structured environment.
This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution ...
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ISBN:
(纸本)9781538621349
This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution law in MFOA for some of the fruit flies improves searching diversity in earlier iterations and increases solution precession in last iterations. In order to apply the PID controllers to the robot manipulator, a nonlinear feedback linearization control technique is employed which can fully linearize and decouple nonlinear robot's dynamics. Simulation results confirm that the MFOA-PID controller can achieve better closed-loop system responses with respect to the original FOA-PID controller.
There are several optimal solutions for a multimodal optimization problem. In fact, a multimodal problem has several optimal points and all these points help to find the solution. In this paper, a new algorithm based ...
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ISBN:
(纸本)9781538625477
There are several optimal solutions for a multimodal optimization problem. In fact, a multimodal problem has several optimal points and all these points help to find the solution. In this paper, a new algorithm based on Cuckoo Optimization Algorithm is presented for solving the multimodal problems. In the proposed algorithm, the multimodal optima are searched in a separate group of cuckoos which have no coevolution relation. The performance of the proposed algorithm on the multimodal testing functions has shown the proper results in finding the optimal points and also reducing the number of function assessments. As a practical matter and closeness to the real world problems, finding the Nash equilibrium points in the noncooperative several-individual games is categorized under hard problems which the mathematical techniques cannot identify all the equilibrium points to solve. The results of proposed method indicate its performance compare to other methods in finding all parts of the Nash equilibrium points.
Research in numerical cognition has led to a widely accepted view of the existence of innate, domain-specific, core numerical knowledge that involves the intraparietal sulcus in the brain. Much of this research has re...
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Research in numerical cognition has led to a widely accepted view of the existence of innate, domain-specific, core numerical knowledge that involves the intraparietal sulcus in the brain. Much of this research has revolved around the ability to perceive and manipulate discrete quantities (e.g., enumeration of dots). We question several aspects of this accepted view and suggest that continuous noncountable dimensions might play an important role in the development of numerical cognition. Accordingly, we propose that a relatively neglected aspect of performancethe ability to perceive and evaluate sizes or amountsmight be an important foundation of numerical processing. This ability might even constitute a more primitive system that has been used throughout evolutionary history as the basis for the development of the number sense and numerical abilities.
Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the searc...
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ISBN:
(纸本)9781509046010
Solving real-world optimization problems is considered a challenging task. This is due to the variability of the characteristics in objective functions, the presence of enormous number of local optima within the search space and highly nonlinear constraints with large number of variables. The advances on this type of problems are of capital importance for many researchers to develop new efficient evolutionary algorithms to tackle such problems in an efficient manner with better solutions. For this reason, this work proposes a new crossover technique based on covariance learning with Euclidean neighborhood which has been incorporated in the basic L-SHADE algorithm. The goal of this new technique is to help L-SHADE establish a suitable coordinate system for the crossover operator. This helps enhance L-SHADE capability to solve real world problems with difficult characteristics and nonlinear constraints. The proposed algorithm, namely L-covnSHADE, is tested on one of the challenging benchmarks which is the IEEE CEC' 11 on real-world numerical optimization problems. This set consists of 22 real-world problems with diverse stimulating characteristics and a dimensionality ranging from 1 to 240 dimensions. The results statistically affirm the efficiency of the proposed approach to obtain better results compared to the L-SHADE algorithm and other state-of-the-art algorithms including the winner of the CEC2011 competition.
We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defense strategies through modeling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer ne...
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ISBN:
(纸本)9781450349390
We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defense strategies through modeling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer networks and use coevolutionary algorithms. In this contribution, we describe RIVALS' current suite of coevolutionary algorithms that use archiving to maintain progressive exploration and that support different solution concepts as fitness metrics. We compare and contrast their effectiveness by executing a standard coevolutionary benchmark (Compare-on-one) and RIVALS simulations on 3 different network topologies. Currently, we model denial of service (DOS) attack strategies by the attacker selecting one or more network servers to disable for some duration. Defenders can choose one of three different network routing protocols: shortest path, flooding and a peer-to-peer ring overlay to try to maintain their performance. Attack completion and resource cost minimization serve as attacker objectives. Mission completion and resource cost minimization are the reciprocal defender objectives. Our experiments show that existing algorithms either sacrifice execution speed or forgo the assurance of consistent results. rIPCA, our adaptation of a known coevolutionary algorithm named IPCA, is able to more consistently produce high quality results, albeit without IPCA's guarantees for results with monotonically increasing performance, without sacrificing speed.
Photovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In ...
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ISBN:
(纸本)9781538627266
Photovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In this work, an energy model for buildings with battery is developed based on electrical consumption and production data. This model takes into account the depth of discharge, state of charge and efficiency over a cycle of a lithium type battery. Three rule-based strategies are then described. This leads to our optimization problem. The optimization is applied on two time slots: one day (for different algorithm strategies) and one week. Robust multi-objective optimization is performed in order to reduce the impact of consumption prediction errors.
The parameterized analysis of bio-inspired computing provides a new way of gaining additional insights into the working behavior of popular approaches such as evolutionary algorithms and ant colony optimization. We gi...
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ISBN:
(纸本)9781538627266
The parameterized analysis of bio-inspired computing provides a new way of gaining additional insights into the working behavior of popular approaches such as evolutionary algorithms and ant colony optimization. We give an overview of two important approaches in this area. The area of parameterized runtime analysis studies the runtime of bio-inspired computing with respect to different parameters of the given problem instance and builds on the success of rigorous runtime analysis of bio-inspired computing in the last 20 years. The feature-based analysis of algorithms for a given optimization problem uses statistical methods to figure out which features of a given problem instance lead to a good or bad performance of the algorithm under consideration. It often uses an evolutionary algorithm for evolving problem instances that exhibit performance differences between a given set of solvers and can be used for effective algorithm selection.
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