Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution. Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algori...

Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC...

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing...

Harmony Search metaheuristic is successfully used in several applications of science and engineering. However, its effectiveness in solving multiobjective optimization problems using the concepts of Pareto optimality, remains unproved. This paper presents two proposals of the Harmony Search metaheuristic for multiobjective optimization, using the ZDT functions as a test bed. Performance metrics...

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve ...

Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework introduced in Tizhoosh (2005), which has become the focus of numerous research init...

This paper studies the one-operator m-machine flow shop scheduling problem with the objective of minimizing the total completion time. In this problem, the processing of jobs and setup of machines require the continuous presence of a single operator. We compare three different mathematical formulations and propose an ant colony optimization based metaheuristic to solve this flow shop scheduling...

The problem of allocating defensive weapon resources to hostile targets is an optimization problem of high military relevance. The need for obtaining the solutions in real-time is often overlooked in existing literature. Moreover, there does not exist much research aimed at comparing the performance of different algorithms for weapon-target allocation. We have implemented a suite of metaheurist...

Nondeterministic metaheuristic optimization and digital image processing are two very different research fields, both extremely active and applicable. They touch in a very limited area, but that narrow interaction opens new very promising applications for digital image processing and new and different deployment of metaheuristic optimization. Multilevel image thresholding is very important for ...

This paper describes an object-oriented software system for continuous optimization by a modified artificial bee colony (ABC) metaheuristic. Karaboga’s ABC algorithm was successfully used on many optimization problems and there is also a corresponding program in C. We implemented a modified version in C# which is easier for maintenance since it is object-oriented and which uses threads and sign...