Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently.
Many optimization problems have been solved using CRO since @cite_12 . Xu used CRO to solve task scheduling problem in grid computing @cite_14 . This problem is a multi-objective NP-hard optimization problem. Lam proposed a population transition problem in P2P live streaming and solved this problem using CRO in @cite_15 . Lam and Li also solved the cognitive radio spectrum allocation problem in @cite_10 . Several variants of CRO were proposed to solve the optimization problem and a self-adaptive scheme was used to control the convergence speed of CRO @cite_10 . Yu proposed a CROANN algorithm from real-coded version of CRO @cite_1 to train artificial neural networks (ANNs). CROANN used a novel stopping criteria to prevent the ANNs from being over-trained and the simulation results demonstrated that CROANN outperformed most previously proposed EA-based ANNs training methods as well as some sophisticated heuristic training methods. This shows that CRO has great potential to tackle different optimization problems like BSDP discussed in this paper.
@cite_14 @cite_1 @cite_15 @cite_10 @cite_12
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In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. Grid computing solves high performance and high-throughput computing problems through sharing resources ranging from personal computers to supercomputers distributed around the world. One of the major problems is task scheduling, i.e., allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resources into account, and task scheduling is formulated as an optimization problem with three objectives. This is an NP-hard problem, and thus, metaheuristic approaches are employed to find the optimal solutions. In this paper, several versions of the Chemical Reaction Optimization (CRO) algorithm are proposed for the grid scheduling problem. CRO is a population-based metaheuristic inspired by the interactions between molecules in a chemical reaction. We compare these CRO methods with four other acknowledged metaheuristics on a wide range of instances. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications. Cognitive radio can help increase the capacity of wireless networks by allowing unlicensed users to use the licensed bands, provided that the occupancy do not affect the prioritized licensed users. One of the fundamental problems in cognitive radio is how to allocate the available channels to the unlicensed users in order to maximize the utility. In this work, we develop an allocation algorithm based on the newly proposed chemical reaction-inspired metaheuristic called Chemical Reaction Optimization (CRO). We study three utility functions for utilization and fairness, with the consideration of the hardware constraint. No matter which utility function is used, simulation results show that the CRO-based algorithm always outperforms the others dramatically. Cognitive radio (CR) is a revolution in radio technology and is viewed as an enabling technology for dynamic spectrum access. This paper investigates how to design distributed algorithm for a future multi-hop CR network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem via a cross-layer optimization approach, with joint consideration of power control, scheduling, and routing. The main contribution of this paper is the development of a distributed optimization algorithm that iteratively increases data rates for user communication sessions. During each iteration, there are two separate processes, a Conservative Iterative Process (CIP) and an Aggressive Iterative Process (AIP). For both CIP and AIP, we describe our design of routing, minimalist scheduling, and power control scheduling modules. To evaluate the performance of the distributed optimization algorithm, we compare it to an upper bound of the objective function, since the exact optimal solution to the objective function cannot be obtained via its mixed integer nonlinear programming (MINLP) formulation. Since the achievable performance via our distributed algorithm is close to the upper bound and the optimal solution (unknown) lies between the upper bound and the feasible solution obtained by our distributed algorithm, we conclude that the results obtained by our distributed algorithm are very close to the optimal solution.