An Overview of Nature Inspired Metaheuristics | Chapter 1 | Recent Developments in Science and Technology for Sustainable Future

Metaheuristics play a crucial role in optimization problem-solving by providing flexible and efficient approaches that can adapt to various complex scenarios. These algorithms, which are general problem-solving strategies, have become increasingly essential in tackling real-world problems where finding an exact solution is often impractical or computationally infeasible. Nature-inspired metaheuristics, in particular, derive inspiration from natural phenomena and the collective behaviors of living organisms, offering innovative solutions that mimic the adaptability and robustness found in nature. Their potential to knock an evenness between exploration and exploitation makes them particularly effective in addressing optimization challenges across diverse realms, including finance, engineering, logistics, and artificial intelligence. Nature-inspired metaheuristics thus stand out as invaluable tools in addressing complex problems where traditional algorithms may fall short, providing a versatile and powerful toolkit for researchers and practitioners alike. Within this chapter, we have presented a succinct overview delving into the realm of nature-inspired metaheuristics. These computational paradigms draw inspiration from the intricate and adaptive systems observed in the natural world. By encapsulating the essence of biological, ecological, and behavioral phenomena, these metaheuristics offer innovative problem-solving approaches that showcase the versatility and efficiency inherent in nature.

Author(s) Details:

Sumita Gulati,
Department of Mathematics, S. A. Jain College, Ambala City, India.

Please see the link here: https://stm.bookpi.org/RDSTSF/article/view/13381

Developing a Hybridized Ant Colony Optimization (HACO) Algorithm: Some Considerations | Chapter 2 | Novel Research Aspects in Mathematical and Computer Science Vol. 8

 In this study, a hybrid ant colony optimization (HACO) method is proposed, whose major goal is to combine the advantages of the AS, ACS, and MMAS previously put out by various scholars at various points in time. Using novel Transition Probability relations with a Jump transition probability relation, the HACO method is used in this paper to solve optimization issues. This approach also identifies the point or path where the desired optimal value has been reached. Additionally, it offers a new pheromone updating rule and a pheromone evaporation residue that determines how much pheromone is still present after updating. This serves as a map for the next ant travelling the trail and other local search techniques. Given that the HACO algorithm has been tested on a variety of combinatorial optimization problems and the results have been proved to favourably compare with analytical results, we note that its computational efficiency quickly uncovers very workable solutions.

Author(s) Details:

Kayode James Adebayo,
Department of Mathematics, Ekiti State University, Ado Ekiti, Ekiti State, Nigeria.

Felix Makanjuola Aderibigbe,
Department of Mathematics, Ekiti State University, Ado Ekiti, Ekiti State, Nigeria.

Adejoke Olumide Dele-Rotimi,
Bamidele Olumilua University of Education, Science and Technology, Ikere Ekiti, Ekiti State, Nigeria.

Please see the link here: https://stm.bookpi.org/NRAMCS-V8/article/view/8356

Bio-Inspired Optimization Based Enhanced Clustering Scheme for Wireless Sensor Networks | Chapter 08 | New Approaches in Engineering Research Vol. 12

 The pervasiveness of wireless sensor networks has made them appropriate for a wide range of critical applications, including environmental surveillance, health monitoring using implantable sensors, weather forecasting, and a variety of other scenarios. Due to the hundreds of thousands of tiny sensor nodes that exist in the networks, key challenges such as computation time, limited memory, and energy are more widespread. In this case, the network's lifespan is entirely dependent on how well available resources are used. The technique of grouping sensor nodes that are close together into clusters makes it easier to administer the cluster and extend the network's lifespan. Swarm intelligence and evolutionary algorithms that pertain to the NP-complete problem are determined at this point to achieve a superior optimal solution. For obtaining extended network lifetime in sensor networks, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed in this research. The benefits of Bacterial Foraging Optimization are integrated in this suggested HABC-BFA-OC technique for boosting the local search potential of the ABC algorithm in order to achieve maximal exploitation and exploration across the parameters evaluated for cluster head selection. During its examination with a varying number of sensor nodes, simulation studies of the suggested HABC-BFA-OC technique confirmed an extended network lifetime with minimal energy consumptions.

Author(S) Details

Bandi Rambabu
CSE, CVR College of Engineering, India.

View Book:- https://stm.bookpi.org/NAER-V12/article/view/3842

A Study on Optimization of Truck-Drone Parcel Delivery Using Metaheuristics | Chapter 12 | Recent Advances in Mathematical Research and Computer Science Vol. 5

 The TSP-D, a version of the travelling salesman problem in drone-based delivery systems, is the subject of this study. The TSP-D is a combinatorial optimization problem in which a truck and a drone work together to deliver packages to clients in the shortest time possible. Because determining the best answer is NP-hard, the number of problems that can be solved optimally is restricted. As a result, metaheuristics are employed to tackle the issue. Metaheuristics are adaptive and intelligent algorithms that have been proven to work in a variety of situations. This paper provides a hybrid meta-heuristic solution to the TSP-D issue, where the answer is based on the Concorde TSP solver's optimal TSP solution. The greedy, randomised adaptive search technique (GRASP) with two local search alternatives is then used to find the delivery routes for the truck and the drone. The application of self-adaptive selection when searching the neighbourhood in GRASP is the work's key contribution. The suggested method was evaluated on 200 cases from the publicly available "Instances of TSP with Drone" benchmark with various parameters. The results were compared to cutting-edge algorithms. In terms of tour duration, nonparametric statistical tests revealed that the proposed strategy is equivalent to rival algorithms (p = 0:074). When the drone and truck are travelling at the same speed ((alpha) = 1), the proposed method performs better or similarly.

Author(S) Details

Sarab AlMuhaideb
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Taghreed Alhussan
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Sara Alamri
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Yara Altwaijry
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Lujain Aljarbou
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

Haifa Alrayes
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11362, Saudi Arabia.

View Book:- https://stm.bookpi.org/RAMRCS-V5/article/view/4977

Bio-Inspired Optimization Based Enhanced Clustering Scheme for Wireless Sensor NetworksBio-Inspired Optimization Based Enhanced Clustering Scheme for Wireless Sensor Networks | Chapter 8 | New Approaches in Engineering Research Vol. 12

The pervasiveness of wireless sensor networks has made them appropriate for a wide range of critical applications, including environmental surveillance, health monitoring via implantable sensors, weather forecasting, and a variety of other scenarios. Because of the hundreds of thousands of tiny sensor nodes that exist in the networks, crucial challenges such as computation time, limited memory, and energy are more widespread. In this case, the network's lifespan is entirely dependent on how well available resources are utilised. The technique of grouping sensor nodes that are close together into clusters is useful for cluster administration and network longevity. At this point, swarm intelligent and evolutionary algorithms related to the NP-complete problem are being investigated in order to find a better optimal solution. A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed in this paper for improving sensor network lifetime. The benefits of Bacterial Foraging Optimization are integrated in this suggested HABC-BFA-OC technique in order to improve the local search potential of the ABC algorithm and achieve maximum exploitation and exploration over the parameters evaluated for cluster head selection. During its research with a varying number of sensor nodes, simulation trials of the suggested HABC-BFA-OC technique confirmed an increased network lifetime with reduced energy usage. As a result, depression and anxiety symptoms are reduced. To ensure that the breathing patterns are correct, the KNN, SVM, Random Forest, and Decision Tree algorithms are used.

Author (S) Details

Bandi Rambabu

CSE, CVR College of Engineering, India.

View Book :- https://stm.bookpi.org/NAER-V12/article/view/3842