Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowl...
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ISBN:
(纸本)9783030336172;9783030336165
Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowledge of the algorithm. A true understanding of ACO is linked to the (social) interactions between the agents given that it is through the interactions that the ants are able to explore-exploit the search space. We propose to study the social interactions that take place as artificial agents explore the search space and communicate using stigmergy. We argue that this study bring insights to the way ACO works. The interaction network that we model out of the social interactions reveals nuances of the algorithm that are otherwise hard to notice. Examples include the ability to see whether certain agents are more influential than others, the structure of communication, to name a few. We argue that our interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.
This paper investigates the applicability of swarm-based algorithms to the game of Tetris. This work proposes an approach to the problem in which neural network weight values are optimized using a particle swarm optim...
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ISBN:
(纸本)9781424481262
This paper investigates the applicability of swarm-based algorithms to the game of Tetris. This work proposes an approach to the problem in which neural network weight values are optimized using a particle swarm optimization (PSO) algorithm. Such an approach has not previously been demonstrated as feasible for Tetris. The reported experimental results show the learning progress of the algorithm, as well as a comparison against a hand-optimized Tetris playing algorithm. The results indicate that the Tetris agents show a continuous improvement over the course of training. Since the experimental focus was on the feasibility of the approach rather than optimizing performance, optimized PSO-based agents were found to be outperformed by the hand-optimized algorithm. However, the playing strategies of the two agents were compared and shown to be similar. The results indicate that a swarm-based approach is feasible, and warrants further investigation.
Computational swarm intelligence has been demonstrably shown to efficiently solve high-dimensional optimization problems due to its flexibility, robustness, and (low) computational cost. Despite these features, swarm-...
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ISBN:
(纸本)9781450383509
Computational swarm intelligence has been demonstrably shown to efficiently solve high-dimensional optimization problems due to its flexibility, robustness, and (low) computational cost. Despite these features, swarm-based algorithms are black boxes whose dynamics may be hard to understand. In this paper, we delve into the Fish School Search (FSS) algorithm by looking at how fish interact within the fish school. We find that the network emerging from these interactions is structurally invariant to the optimization of three benchmark functions: Rastrigin, Rosenbrock and Schwefel. However, at the same time, our results also reveal that the level of social interactions among the fish depends on the problem. We show that the absence of highly-influential fish leads to a slow-paced convergence in FSS and that the changes in the intensity of social interactions enable good performance on both unimodal and multimodal problems. Finally, we examine two other swarm-based algorithms-the Artificial Bee Colony (ABC) and Particle swarm Optimization (PSO) algorithms-and find that for the same three benchmark functions, the structural invariance characteristic only occurs in the FSS algorithm. We argue that FSS, ABC, and PSO have distinctive signatures of interaction structure and flow.
Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based alg...
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Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. While flying, a group of geese forms themselves as a "V"configuration. In this way, the geese in the front can minimize the air resistance of the ones in the back. This allows the geese to fly around 70% farther as a group than they could individually. The GGO algorithm is first validated by being applied to nineteen datasets retrieved from the UCI Machine Learning Repository. Each dataset contains a varied amount of characteristics, instances, and classes that are used to choose features. After that, it is put to use in the process of solving a number of engineering benchmark functions and case studies. Several case studies are solved using the proposed algorithm too, including the pressure vessel design and the tension/compression spring design. The findings demonstrate that the GGO method outperforms numerous other comparative optimization algorithms and delivers superior accuracy compared to other algorithms. The results of the statistical analysis tests, such as Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), demonstrate that the GGO algorithm achieves superior results.
Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by...
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Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols.
This work introduces"(hssolf)",a novel hybrid optimization *** is a hybrid of"sperm swarm optimization(SSO)"and"Levi flight mechanism (LF)".The sperm swarm algorithm has strong search abi...
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This work introduces"(hssolf)",a novel hybrid optimization *** is a hybrid of"sperm swarm optimization(SSO)"and"Levi flight mechanism (LF)".The sperm swarm algorithm has strong search ability,but it is prone to"premature phenomenon",and the convergence speed falls as evolution *** hybrid nonlinear decreasing Levy flight may keep sperm swarm from falling into the local optimal value,shorten the later stagnation time,and increase convergence *** 'assembly on Evolutionary Computing (CEC)' 2017 suite is used to evaluate the proposed algorithm with the sperm swarm algorithm (SSO) and (hssogsa).In addition,we conduct extensive research on a multimodal *** proposed hybrid sperm swarm algorithm based on Levi flight may significantly increase solution accuracy,accelerate the convergence speed,and has a superior optimization impact.
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