This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE′–HJ, which is a modification of MA–MDE′ in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE′–IHS–HJ, which...

Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems. Multiple natural frequency constraints normally cause difficult dynamic sensitivity analysis and, in addition, two different types of design variables, nodal coordinates and crosssectional areas, often lead to divergence. Thus, the choice of the appropriated meth...

Ant Colony Optimization is one of the metaheuristic algorithms and first member of ACO is Ant System (AS). AS uses a population of co-operating ants also known as agents. The cooperation phenomenon among the ants is called foraging and recruiting behavior. This describes how ants explore the world in search of food sources, then find their way back to the nest and indicate the food source to th...

The indirect communication and foraging behavior of certain species of ants has inspired a number of optimization algorithms for NP-hard problems. These algorithms are nowadays collectively known as the ant colony optimization (ACO) metaheuristic. This chapter gives an overview of the history of ACO, explains in detail its algorithmic components and summarizes its key characteristics. In additi...

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning ...

Turing’s pioneer work in heuristic search has inspired many generations of research in heuristic algorithms. In the last two decades, metaheuristic algorithms have attracted strong attention in scientific communities with significant developments, especially in areas concerning swarm intelligence based algorithms. In this work, we will briefly review some of the important achievements in metahe...

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more ―standard‖ algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could...

Todays engineering research is highly motivated towards the nature inspired metaheuristic computational algorithm as they have the capability to give better results as compared to the conventional methods. Wireless Sensor Networks(WSNs) have become increasingly popular due to their extensive array of applications. Desing of energy efficient routing algorithms is an important issue in the design...

Instances of constraint satisfaction problems can be solved efficiently if they are representable as a tree decomposition of small width. Unfortunately, the task of finding a decomposition of minimum width is NP-complete itself. Therefore, several heuristic and metaheuristic methods have been developed for this problem. In this paper we investigate the application of different variants of Ant C...