Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm

© 2019 IEEE. Several sensing problems in wireless sensor networks (WSNs) can be modeled to maximum satisfaction (MAX-SAT) or SAT problems. Also, MAX-SAT is an established framework for computationally expensive problems in other fields. There exist efficient algorithms to solve the MAX-SAT, which is an NP-hard problem. The reason for remodeling various sensing problems to MAX-SAT is to use these algorithms to solve challenging sensing problems. In this paper, we test a binary Biogeography-based (BBBO) algorithm for the MAX-SAT as an optimization problem with a binary search space. The original BBO is a swarm intelligence-based algorithm, which is well-tested for continuous (and nonbinary) integer space optimization problems, but its use for the binary space was limited. Since the exact algorithm to solve the MAX-SAT problem using moderate computing resources is not well-known; therefore, swarm intelligence based evolutionary algorithms (EAs) can be helpful to find better approximate solutions with limited computing resources. Our simulation results demonstrate the experimental exploration of the binary BBO algorithm against binary (enhanced fireworks algorithm) EFWA, discrete ABC (DisABC) and Genetic Algorithm (GA) for several classes of MAX-SAT problem instances

Planning Capacity for 5G and Beyond Wireless Networks by Discrete Fireworks Algorithm With Ensemble of Local Search Methods

In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources

Applications of fireworks-based evolutionary algorithms for computationally challenging network problems

This thesis covers two types of contributions: formulation of network optimization problems and algorithms to solve these optimization problems. We propose resource assignment problem in Internet of Things network (IoTN) with three nodes: IoT, core cluster node (CCN) and base station (BS). The assignment of resources, such as CPU and memory, from IoTs to CCNs, and CCNs to BSs is a challenging task. The objective of the problem is to minimize the weighted sum of computational power at CCNs and transmission power between IoTs-CCNs and CCNs-BSs radio connections. We also propose a broadband wireless network (BWN) wherein the planning of BSs, relay stations (RSs), and their connections to subscribers minimizes the overall (i.e., weighted sum of the hardware and operational) cost of the network and reformulate a virtual machine (VM) placement to minimize power consumption in a datacenter. The (re)formulated problems are integer programming problem and finding optimal solutions for these problems by using exhaustive search is not practical due to demand of high computing resources. The practical approach is to minimize power in IoT network and VM placement, and plan broadband wireless network using population-based heuristic algorithms. We propose swarm intelligence-based algorithms, that is, two versions of the discrete fireworks algorithm (DFWA) and its variants. The performance of these new algorithms is compared against the low-complexity Biogeography-based Optimization (LC-BBO) algorithm, the Discrete Artificial Bee Colony (DABC) algorithm, and the Genetic Algorithm (GA). Our simulation results and statistical test demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources