MOCCA-II: A multi-objective co-operative co-evolutionary algorithm

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverageaware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered.

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“…It has been widely used because of its simplicity. However, it is known that this approach has the following problems [99] [100] [101] [102]:…”

Section: F4 Multi-objective Approachmentioning

confidence: 99%

Coverage protocols for wireless sensor networks: Review and future directions

2019

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“…By executing intercommunication of subpopulations residing in the distributed system and incorporating archiving, dynamic sharing, and extending operators, the algorithm is able to efficiently approximate solutions uniformly along the Pareto front. Other dMOEAs based on divide-and-conquer and coevolution techniques can be found in [29,152].…”

Section: Distributed Evolutionary Multiobjective Optimizationmentioning

confidence: 99%

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Gong

Chen

Zhan

et al. 2015

Applied Soft Computing

335

132

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a b s t r a c tThe increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

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“…CGGA is a population-based evolution-guided stochastic search technique which inspired by symbiotic interactions where different species live together in a mutually beneficial relationship [8]. And it has been applied in the domains such as job shop scheduling [4], air traffic and capability planning [8] and so on.…”

Section: Ccgamentioning

confidence: 99%

“…And it has been applied in the domains such as job shop scheduling [4], air traffic and capability planning [8] and so on. Comparing to GA, all parameters of the fitness function in CCGA are not encoded and represented by a single chromosome [6].…”

Section: Ccgamentioning

confidence: 99%

Cooperative Coevolutionary Genetic Algorithm For Water Resources Optimization Model And Its Application

Xu¹,

Dong²,

Hao³

et al. 2017

Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC

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To solve the complexity of multi-public sources water allocation, the optimization model using the multi-objective function and multi-constrained conditions of the cooperative coevolution genetic algorithm (CCGA) was proposed for water resources management. The objective function was the highest benefit to both economy and society, while the constraints included available water supply amount, water demand, water supply projects capacity, ecology protection and non-negativity. Water demand ratio parameters for each public source were proposed which could be optimized by CCGA. The case study in Suibianhe Region showed that the proposed model was capable of allocating water resource efficiently and can provide useful information for water resources integrated management.

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MOCCA-II: A multi-objective co-operative co-evolutionary algorithm

Cited by 29 publications

References 23 publications

Coverage protocols for wireless sensor networks: Review and future directions

Coverage protocols for wireless sensor networks: Review and future directions

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Cooperative Coevolutionary Genetic Algorithm For Water Resources Optimization Model And Its Application

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