scholarly journals A Bacterial Chemotaxis-Inspired Coordination Strategy for Coverage and Aggregation of Swarm Robots

2021 ◽  
Vol 11 (3) ◽  
pp. 1347
Author(s):  
Laihao Jiang ◽  
Hongwei Mo ◽  
Peng Tian
Keyword(s):  
Optimization Algorithm ◽  
Fitness Function ◽  
Bacterial Chemotaxis ◽  
Superior Performance ◽  
Bacterial Foraging Optimization ◽  
Random Factor ◽  
Bacterial Colony ◽  
Coordination Strategies ◽  
Coordination Strategy ◽  
Swarm Robots

Many bio-inspired coordination strategies have been investigated for swarm robots. Bacterial chemotaxis exhibits a certain degree of intelligence, and has been developed some optimization algorithm for robot(s), e.g., bacterial foraging optimization algorithm (BFOA) and bacterial colony chemotaxis optimization algorithm (BCC). This paper proposes a bacterial chemotaxis-inspired coordination strategy (BCCS) of swarm robotic systems for coverage and aggregation. The coverage is the problem of finding a solution to uniformly deploy robots on a given bounded space. To solve this problem, this paper uses chaotic preprocessing to generate the initial positions of the robots. After the initialization, each robot calculates the area only covered by itself as the fitness function value. Then, each robot makes an action, running or rotating, depending on coordination strategy inspired bacterial chemotaxis. Moreover, we extend this solution and introduce a random factor to overcome aggregation, which is to guide robots to rendezvous at an unspecified point. The simulation results demonstrate the superior performance of the proposed coordination strategy in both success rate and an average number of iterations than other controllers.

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

2021 ◽  
pp. 1-19
Author(s):  
Nagaraju Pamarthi ◽  
N. Nagamalleswara Rao
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Privacy Preservation ◽  
Fitness Function ◽  
Information Loss ◽  
Superior Performance ◽  
Data Matrix ◽  
Sensitive Information ◽  
Bilinear Map ◽  
Ant Lion

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

scholarly journals Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Danilo S. da Cunha ◽  
Rafael S. Xavier ◽  
Daniel G. Ferrari ◽  
Fabrício G. Vilasbôas ◽  
Leandro N. de Castro
Keyword(s):  
Data Model ◽  
Association Rule ◽  
Clonal Selection ◽  
Superior Performance ◽  
Clonal Selection Algorithm ◽  
Stream Data ◽  
Rule Mining ◽  
Bacterial Foraging Optimization ◽  
Bacterial Colony ◽  
Static Data

Bacterial colonies perform a cooperative and distributed exploration of the environmental resources by using their quorum-sensing mechanisms. This paper describes how bacterial colony networks and their skills to explore resources can be used as tools for mining association rules in static and stream data. A new algorithm is designed to maintain diverse solutions to the problems at hand, and its performance is compared to that of other well-known bacteria, genetic, and immune-inspired algorithms: Bacterial Foraging Optimization (BFO), a Genetic Algorithm (GA), and the Clonal Selection Algorithm (CLONALG). Taking into account the superior performance of our approach in static data, we applied the algorithms to dynamic environments by converting static into flow data via a stream data model named sliding-window. We also provide some notes on the running time of the proposed algorithm using different hardware and software architectures.

scholarly journals hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

2021 ◽  
Author(s):  
Sümeyya İlkin ◽  
Tuğrul Hakan Gençtürk ◽  
Fidan Kaya Gülağız ◽  
Hikmetcan Özcan ◽  
Mehmet Ali Altuncu ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Optimization Algorithm ◽  
Bacterial Colony ◽  
Melanoma Detection

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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