Nature-Inspired Metaheuristic Algorithms Research Papers (original) (raw)

In contemporary times, Wireless Body Area Networks has arisen as a new area for tracking, identification, examining and treatment of multiple deadly diseases in patients. It is designed by grouping biosensors that might either be implanted or fixed outwards over anyone's body, for scrutinization of myriad body parts. In it, the biosensor nodes are pinioned to different body parts to examine the health or specific bodily functions such as blood pressure, respiratory levels, heart rate, etc. Energy utilizations prove to be major roadblock for WBANs as replacing, re-energizing these biosensors might cause discomfortment for the person bearing them. The contemporary research is mainly centered on minimizing the energy utilization in body area networks. For the purpose of energy conservation, we append QPSO, in order to find the shortest, energy-efficient route, in our wireless body area network. The proposed technique uses a relay node along with a multiple-hops technique. For the purpose of achieving better results, a quantum behaved PSO based routing protocol (QRP) is put forth to optimize a fitness function. The performance of QRP is analyzed and compared with existing protocols namely, M-ATTEMPT, SIMPLE and PSOBAN. Our experimental results show significant improvements in packet success rates as our proposed protocol achieves an increased throughput over the aforementioned protocols. Similarly, our protocol achieves much higher transmission rates, lesser path loss and retains more residual energy as well.

- by
- •
- Machine Learning, Optimization, Wireless Body Area Network (WBAN), Metaheuristic Algorithms

Bin Packing Problem (BPP) is a Combinatorial Optimization problem, which is used to find the optimal object from a finite set of objects. The purpose of BPP is to pack the items with different weight into finite number of bins without exceeding its capacity. The main objective of this problem is to minimize the number of bins used and pack the items efficiently. This paper reviews a general idea of BPP and various algorithms which are used to solve the BPP. In this work, two heuristic algorithms First-Fit algorithm and Best-Fit algorithm are implemented and tested with well-known benchmark instances. This paper also discusses the algorithms which are inspired by Biology, such as Ant Colony Optimization algorithm (ACO), Cuckoo search and Genetic algorithm and their applications to Combinatorial Optimization problems.

This paper introduces a strategy to enrich swarm intelligence algorithms with the preferences of the Decision Maker (DM) represented in an ordinal classifier based on interval outranking. Ordinal classification is used to bias the search toward the Region of Interest (RoI), the privileged zone of the Pareto frontier containing the most satisfactory solutions according to the DM’s preferences. We applied this hybridising strategy to two swarm intelligence algorithms, i.e., Multi-objective Grey Wolf Optimisation and Indicator-based Multi-objective Ant Colony Optimisation for continuous domains. The resulting hybrid algorithms were called GWO-InClass and ACO-InClass. To validate our strategy, we conducted experiments on the DTLZ problems, the most widely studied test suit in the framework of multi-objective optimisation. According to the results, our approach is suitable when many objective functions are treated. GWO-InClass and ACO-InClass demonstrated the capacity of reaching the RoI better than the original metaheuristics that approximate the complete Pareto frontier.