Cooperation Of Operators Research Articles

Performance analysis of two-way multi-users cooperative communication system based on GSPIM-DCSK scheme

Based on the Joint Grouping Subcarrier-Permutation Index Modulation-DCSK (GSPIM-DCSK) system, we provide a new two-way multi-user half-duplex cooperative communication system in this work. Given its many benefits, the GSPIM-DCSK modulation is specifically selected as a choice for network coding techniques. A permutation index is used in the GSPIM-DCSK system to incorporate additional data bits with the modulating and carrier index bits. Establishing a cooperative multiuser system where users attempt to make contact with one another over a common relay is the main goal of this effort. Multi-user nodes and a single relay with a cooperative operator on staff for decoding and forwarding make up this system. To avoid interference from other users, the user’s nodes multiplex across various time intervals. We derive analytical expressions for the average bit error rate (BER) of the GSPIM-DCSK multi-user cooperative communication system in the presence of additive white Gaussian noise (AWGN) and multiple paths Rayleigh fading channels. The analytical and simulated average BER findings have been contrasted to verify the accurateness of the average BER analysis. Additionally, a comparison is presented between the average bit error rate (BER) performance of a novel GSPIM-DCSK cooperative system and an established DCSK cooperative system. The throughput and link spectral efficiency of this novel cooperative system are analyzed and compared with those of existing traditional cooperative systems. For the two-way two-user cooperative system, this comparison is done with two-way two-user systems like DCSK-CC and one-way cooperative system based on CIM-SR-DCSK-CC; for the two-way three-user cooperative system, it is done with scheme1 and scheme2. To compare and assess the performance of various systems, simulation results are employed. Furthermore, an analysis demonstrates how the proposed cooperative system, when combined with GSPIM-DCSK techniques, improves the efficiency of conventional cooperative DCSK systems.

Cooperative linear regression model for image set classification

In image set classification, affine hull based models have achieved good performance. However, these models only focus on holistic images and ignore the local information of images. To incorporate local information, this paper develops a cooperative operator and proposes a cooperative linear regression model (CLRM) for image set classification. Specifically, CLRM first proposes a block partitioning strategy to obtain local information. Image sets and local information are both modeled as affine hulls. Then, a linear regression model embedded with cooperative operator is proposed to incorporate different information and compute the distance between the different affine hulls. Finally, a combined distance measure is proposed to incorporate different information for classification. Extensive experiments on different recognition tasks such as face recognition on image/video based databases and object classification show that the proposed cooperative operator can improve the performance of the model and the proposed CLRM achieves competitive performance compared with the state-of-the-art classifiers.

A human learning optimization algorithm with competitive and cooperative learning

Human learning optimization (HLO) is a simple yet powerful metaheuristic developed based on a simplified human learning model. Competition and cooperation, as two basic modes of social cognition, can motivate individuals to learn more efficiently and improve their efficiency in solving problems by stimulating their competitive instincts and increasing interaction with each other. Inspired by this fact, this paper presents a novel human learning optimization algorithm with competitive and cooperative learning (HLOCC), in which a competitive and cooperative learning operator (CCLO) is developed to mimic competition and cooperation in social interaction for enhancing learning efficiency. The HLOCC can efficiently maintain the diversity of the algorithm as well as achieve the optimal values, demonstrating that the proposed CCLO can effectively improve algorithm performance. HLOCC has been compared with other heuristic algorithms on CEC2017 functions. In the second study, the uncapacitated facility location problems (UFLPs) which are one of the pure binary optimization problems are solved with HLOCC. The experimental results show that the developed HLOCC is superior to previous HLO variants and other metaheuristics with its improved exploitation and exploration abilities.

Expanding narratives of governance constraints to improve coral reef conservation.

To understand and address the failures of reef governance, it is critical to understand the perceptions of diverse policy makers and practitioners about the challenges they face in achieving their goals. Examining the discourse of policy makers and practitioners can reveal the extent to which these perceptions capture the full spectrum of potential governance challenges, including those related to management, institutional structures and processes, the values and principles underpinning governance, and the social and environmental context. We conducted semistructured interviews with 110 policy makers and practitioners across multiple sectors, scales, and contexts in Barbados, St Kitts and Nevis, Belize, and Honduras. We used thematic qualitative analysis informed by theories of interactive governance and governability to examine the challenges perceived by governance actors. Perceived governance challenges were broadly consistent across countries, but differed by sector (V = 0.819, F6,60 = 1.502, p = 0.01) and by level (community compared with national) (V = 0.194, F1,10 = 2.178, = 0.026). Management inputs and outputs, challenges relating to the socioeconomic context, issues of leadership and power, and stakeholder engagement were commonly cited challenges (>75%). Few respondents discussed challenges relating to the ecological context, governance processes, or the values and principles underpinning governance. We argue that examining perceptions can inform efforts to improve governance and assess the appropriateness of particular management tools under context-specific governance constraints. Furthermore, expanding the narratives of governance challenges to encompass the subtle values and images underpinning governance, and the scale of the challenges faced, can help identify a wider set of opportunities for change.

5G heterogeneous network selection and resource allocation optimization based on cuckoo search algorithm

In order to solve the problem of spectrum resource shortage and high-speed access in 5G network, the multi-agent system is embedded into the standard cuckoo algorithm, and the multi-agent cuckoo algorithm is proposed. Firstly, the spectrum information of network channel sharing available to users is obtained, and the cuckoo search algorithm is used to optimize under the condition of satisfying the quality of service (QoS) guarantee of users, and the optimal allocation scheme is obtained by iterating for many times. The use steps are illustrated by examples. Compared with the traditional genetic algorithm, the calculation complexity can be reduced, and it can also be extended to more users and networks. In this algorithm, each cuckoo represents an agent, and all the agents constitute a von-Neumann structure. Through the neighborhood competition cooperation operator, mutation operator, self-learning operator and the evolution mechanism of cuckoo algorithm, they can continuously enhance the energy and improve the adaptability, and can quickly and accurately find the optimal solution of the problem.

Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices

To decrease the load of electricity grid during the peak period, time-of-use electricity price has been implemented in industries to shift the production activities from the peak to the off-peak period. This paper addresses the flexible job shop scheduling under time-of-use electricity prices (FJSPTOUEP) to minimize both makespan and total electricity cost (TEC) simultaneously. We present the mixed integer programming model and propose a hybrid multi-objective evolutionary algorithm based on decomposition (HMOEA/D) to solve the problem. To generate an initial population with certain quality and diversity, several rules are used together. In the framework of MOEA/D, a cooperative search operator is designed to generate new solutions by exchanging information of neighbours. To improve the quality of solutions, two local intensification operators are designed by analysing the critical path of the schedule. An adaptive selection strategy is designed based on the reference point for well using the local search operators to enhance exploitation ability. Moreover, according to the characteristics of time-of-use electricity prices, an adjustment strategy is proposed to reduce electricity cost for further improving solutions. Computational results and statistical comparisons show that both the local intensification and adjustment strategy are effective. It also shows that the proposed HMOEA/D is more effective than other optimization algorithms in solving the FJSPTOUEP.

Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem

<p style='text-indent:20px;'>The job-shop scheduling problem is one of the well-known hardest combinatorial optimization problems. The problem has captured the interest of a significant number of researchers, but no efficient solution algorithm has been found yet for solving it to optimality in polynomial time. In this paper, a hybrid social-spider optimization algorithm with differential mutation operator is presented to solve the job-shop scheduling problem. To improve the exploration capabilities of the social spider optimization algorithm (SSO), we incorporate the DM operator (a mutation operator taken from the deferential evolutionary (DE) algorithm) into the framework of the female cooperative operator. The experimental results show that the proposed method effectiveness in solving job-shop scheduling compared to other optimization algorithms in the literature.

Cloud-Based Multi-Agent Cooperation for IoT Devices Using Workflow-Nets

Most Internet of Things (IoT)-based service requests require excessive computation which exceeds an IoT device’s capabilities. Cloud-based solutions were introduced to outsource most of the computation to the data center. The integration of multi-agent IoT systems with cloud computing technology makes it possible to provide faster, more efficient and real-time solutions. Multi-agent cooperation for distributed systems such as fog-based cloud computing has gained popularity in contemporary research areas such as service composition and IoT robotic systems. Enhanced cloud computing performance gains and fog site load distribution are direct achievements of such cooperation. In this article, we propose a workflow-net based framework for agent cooperation to enable collaboration among fog computing devices and form a cooperative IoT service delivery system. A cooperation operator is used to find the topology and structure of the resulting cooperative set of fog computing agents. The operator shifts the problem defined as a set of workflow-nets into algebraic representations to provide a mechanism for solving the optimization problem mathematically. IoT device resource and collaboration capabilities are properties which are considered in the selection process of the cooperating IoT agents from different fog computing sites. Experimental results in the form of simulation and implementation show that the cooperation process increases the number of achieved tasks and is performed in a timely manner.

A Fireworks Algorithm for the System-Level Fault Diagnosis Based on MM* Model

Aiming at the characteristics of MM* model fault diagnosis, a fireworks algorithm based on a dual population strategy is designed. The dual population of the algorithm is operated independently in parallel, and cooperative operator and optimal operator are cross-executed in the iterative process. The cooperative operator enables two populations to exchange effective information, avoiding the premature maturity of the algorithm. The optimal operator helps to strengthen the global search power of the algorithm and improve the convergence rate of the algorithm. At the same time, the constraint equation is designed, a new fitness function is proposed, and the mutation operator and selection strategy are optimized. The experimental comparison shows that the algorithm improves the efficiency and accuracy of system-level fault diagnosis and has good practicability. Finally, the correctness of the algorithm is proved by theory, and the time complexity of the algorithm is analyzed.

Competitive Energy Trading Framework for Demand-Side Management in Neighborhood Area Networks

This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully competitive CES operator in a non-cooperative Stackelberg game, a benevolent CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully competitive CES model gives the best tradeoff of operating environment between the CES operator and the users.

基于缩放因子和协作算子的肾脏算法

基于缩放因子和协作算子的肾脏算法

Workflow-Net Based Service Composition Using Mobile Edge Nodes

Content delivery through cloud networks has gained popularity due to the cloud’s ability to provide on-demand services. However, composite services, such as customized multimedia content, introduce both delays and resource limitations if traditional cloud solutions are used. With recent advances in mobile edge computing, customized media delivery can be achieved through compositions of service specific overlays (SSOs). This paper presents a workflow-net-based mechanism for mobile edge node cooperation in fog-cloud networks to form guaranteed SSOs. The proposed solution uses a mathematical cooperation operator to turn the SSO composition problem expressed as workflow nets into algebraic representations. In turn, the minimal cost cooperative path from the workflow net is determined such that it guarantees the delivery of the requested composite media services to clients. Experimental results show that the composition process can be adequately established and carried out in a timely manner.

SAMCCTLBO: a multi-class cooperative teaching–learning-based optimization algorithm with simulated annealing

A variant of teaching---learning-based optimization algorithm (TLBO) with multi-classes cooperation and simulated annealing operator (SAMCCTLBO) is proposed in paper. To take full advantage of microteaching, the population is divided into several sub-classes, the mean of all learners in teacher phase of original TLBO is replaced by the mean solutions of different sub-classes, the modification might make the mean solutions improved quickly for the effect of microteaching is often better than teaching in big classes. With considering the limitation of learning ability of learner, the learners in different sub-classes only learn new knowledge from others in their sub-classes in learner phase of SAMCCTLBO, and all learners are regrouped randomly after some generations to improve the diversity of the sub-classes. The diversity of the whole class is improved by simulated annealing operator. The effectiveness of the proposed algorithm is tested on several benchmark functions, the results demonstrate that SAMCCTLBO has some good performances when compared with some other EAs.

Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems

This paper proposes an information sharing mechanism (ISM) to improve the performance of particle swarm optimization (PSO). The ISM allows each particle to share its best search information, so that all the other particles can take advantage of the shared information by communicating with it. In this way, the particles could enhance the mutual interaction with the others sufficiently and heighten their search ability greatly by using the search information of the whole swarm. Also, a competitive and cooperative (CC) operator is designed for a particle to utilize the shared information in a proper and efficient way. As the ISM share the search information among all the particles, it is an appropriate way to mix up information of the whole swarm for a better exploration of the landscape. Therefore, the competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems. The satisfactory performance of CCPSO-ISM is evaluated by comparing it with other variants of PSOs on a set of 16 global optimization functions. Moreover, the effectiveness and efficiency of CCPSO-ISM is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality.

A multipopulation coevolutionary strategy for multiobjective immune algorithm.

How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems.

Workflow Nets for Multiagent Cooperation

We present a formal framework for robotic cooperation in which we use an extension to Petri nets, known as workflow nets, to establish a protocol among mobile agents based on the task coverage they maintain. Our choice is motivated by the fact that Petri nets handle concurrency and that goal reachability, or soundness, can be theoretically established. In particular, we define a mathematical cooperation operator which turns cooperation problems expressed as workflow nets into algebraic representations. While we do not address the problem of efficiency, we formally demonstrate that this framework guarantees soundness, or goal reachability, using workflow nets.

A semi-quantitative equivalence for abstracting from fast reactions

Semantic equivalences are used in process algebra to capture the notion of similar behaviour, and this paper proposes a semi-quantitative equivalence for a stochastic process algebra developed for biological modelling. We consider abstracting away from fast reactions as suggested by the Quasi-Steady-State Assumption. We define a fast-slow bisimilarity based on this idea. We also show congruence under an appropriate condition for the cooperation operator of Bio-PEPA. The condition requires that there is no synchronisation over fast actions, and this distinguishes fast-slow bisimilarity from weak bisimilarity. We also show congruence for an operator which extends the reactions available for a species. We characterise models for which it is only necessary to consider the matching of slow transitions and we illustrate the equivalence on two models of competitive inhibition.

Cycling expression and cooperative operator interaction in the trp operon of Escherichia coli

Oscillatory behaviour in the tryptophan operon of an Escherichia coli mutant strain lacking the enzyme-inhibition regulatory mechanism has been observed by Bliss et al. but not confirmed by others. This behaviour could be important from the standpoint of synthetic biology, whose goals include the engineering of intracellular genetic oscillators. This work is devoted to investigating, from a mathematical modelling point of view, the possibility that the trp operon of the E. coli inhibition-free strain expresses cyclically. For that we extend a previously introduced model for the regulatory pathway of the tryptophan operon in Escherichia coli to account for the observed multiplicity and cooperativity of repressor binding sites. Thereafter we investigate the model dynamics using deterministic numeric solutions, stochastic simulations, and analytic studies. Our results suggest that a quasi-periodic behaviour could be observed in the trp operon expression level of single bacteria.

Crystal structure of the λ repressor and a model for pairwise cooperative operator binding

Bacteriophage lambda has for many years been a model system for understanding mechanisms of gene regulation. A 'genetic switch' enables the phage to transition from lysogenic growth to lytic development when triggered by specific environmental conditions. The key component of the switch is the cI repressor, which binds to two sets of three operator sites on the lambda chromosome that are separated by about 2,400 base pairs (bp). A hallmark of the lambda system is the pairwise cooperativity of repressor binding. In the absence of detailed structural information, it has been difficult to understand fully how repressor molecules establish the cooperativity complex. Here we present the X-ray crystal structure of the intact lambda cI repressor dimer bound to a DNA operator site. The structure of the repressor, determined by multiple isomorphous replacement methods, reveals an unusual overall architecture that allows it to adopt a conformation that appears to facilitate pairwise cooperative binding to adjacent operator sites.

Asymptotic behaviour of principal eigenvalues for a class of cooperative systems

This paper analyzes the asymptotic behaviour as λ ↑ ∞ of the principal eigenvalue of the cooperative operator L ( λ ) : = ( L 1 + λ a ( x ) − b − c L 2 + λ d ( x ) ) in a bounded smooth domain Ω of R N , N ⩾ 1 , under homogeneous Dirichlet boundary conditions on ∂ Ω, where a ⩾ 0 , d ⩾ 0 , and b ( x ) > 0 , c ( x ) > 0 , for all x ∈ Ω ¯ . Precisely, our main result establishes that if Int ( a + d ) −1 ( 0 ) consists of two components, Ω 0 , 1 and Ω 0 , 2 , then lim λ ↑ ∞ σ 1 [ L ( λ ) ; Ω ] = min i ∈ { 1 , 2 } σ 1 [ L ( 0 ) ; Ω 0 , i ] , where, for any D ⊂ Ω and λ ∈ R , σ 1 [ L ( λ ) ; D ] stands for the principal eigenvalue of L ( λ ) in D. Moreover, if we denote by ( φ λ , ψ λ ) the principal eigenfunction associated to σ [ L ( λ ) ; Ω ] , normalized so that ∫ Ω ( φ λ 2 + ψ λ 2 ) = 1 , and, for instance, σ 1 [ L ( 0 ) ; Ω 0 , 1 ] < σ 1 [ L ( 0 ) ; Ω 0 , 2 ] , then the limit ( Φ , Ψ ) : = lim λ ↑ ∞ ( φ λ , ψ λ ) is well defined in H 0 1 ( Ω ) × H 0 1 ( Ω ) , Φ = Ψ = 0 in Ω ∖ Ω 0 , 1 and ( Φ , Ψ ) | Ω 0 , 1 provides us with the principal eigenfunction of σ [ L ( 0 ) ; Ω 0 , 1 ] . This is a rather striking result, for as, according to it, the principal eigenfunction must approximate zero as λ ↑ ∞ if a + d > 0 , in spite of the cooperative structure of the operator.