Enhanced machining features and multi-objective optimization of CNT mixed-EDM process for processing 316L steel

In recent years, feature selection has emerged as a major challenge in machine learning. In this paper, considering the promising performance of metaheuristics on different types of applications, six physics-inspired metaphor algorithms are employed for this problem. To evaluate the capability of dimensionality reduction in these algorithms, six diverse-natured datasets are used. The performance is compared in terms of the average number of features selected (AFS), accuracy, fitness, convergence capabilities, and computational cost. It is found through experiments that the accuracy and fitness of the Equilibrium Optimizer (EO) are comparatively better than the others. Finally, the average rank from the perspective of average fitness, average accuracy, and AFS shows that EO outperforms all other algorithms.

/pdf/analyzing-physics-inspired-metaheuristic-algorithms-in-212cy494.pdf

Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor

Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09955508.pdf

Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems

Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization. Note that the MATLAB source codes for GTO will be publicly available after the acceptance of the paper. 

/pdf/giant-trevally-optimizer-gto-a-novel-metaheuristic-algorithm-31hhqj7d.pdf

Abstract It is vital to optimize the drilling trajectory to reduce the possibility of drilling accidents and boosting the efficiency. Previously, the wellbore trajectory was optimized using the true measured depth and well profile energy as objective functions without considering uncertainty between the actual and planned trajectories. Without an effective management of the uncertainty associated with trajectory planning, the drilling process becomes more complex. Prior techniques have some drawbacks; for example, they could not find isolated minima and have a slow convergence rate when dealing with high-dimensional problems. Consequently, a novel approach termed the “Modified Multi-Objective Cellular Spotted Hyena Optimizer” is proposed to address the aforesaid concerns. Following that, a mechanism for eliminating outliers has been developed and implemented in the sorting process to minimize uncertainty. The proposed algorithm outperformed the standard methods like cellular spotted hyena optimizer, spotted hyena optimizer, and cellular grey wolf optimizer in terms of non-dominated solution distribution, search capability, isolated minima reduction, and pareto optimal front. Numerous statistical analyses were undertaken to determine the statistical significance of the algorithm. The proposed algorithm achieved the lowest inverted generational distance, spacing metric, and error ratio, while achieving the highest maximum spread. Finally, an adaptive neighbourhood mechanism has been presented, which outperformed fixed neighbourhood topologies such as L5, L9, C9, C13, C21, and C25. Afterwards, the technique for order preference by similarity to ideal solution and linear programming technique for multidimensional analysis of preference were used to provide the best pareto optimal solution. 

/pdf/uncertainty-handling-in-wellbore-trajectory-design-a-j49fygks.pdf

Uncertainty handling in wellbore trajectory design: a modified cellular spotted hyena optimizer-based approach

Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.

Ashikur Rahman

Papers

Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances