Metaheuristic algorithms are traditionally designed following a manual, iterative algorithm development process. While this process sometimes leads to high performing algorithms, it is labor-intensive, error-prone, difficult to reproduce, and explores only a limited number of design alternatives. In this article, we advocate the automatic design of hybrid metaheuristic algorithms. For an effect...

The difficulty in predicting early cancer is due to the lack of illness indicators. Metaheuristic approaches are a family algorithms that seek find optimal values for uncertain problems with several implications optimization and classification problems. An automated system recognizing illnesses can respond accuracy, efficiency, speed, helping medical professionals spot abnormalities lowering de...

The problem of finding optimal locations of base stations, their pilot powers and channel assignments in UMTS mobile networks belongs to a class of NP-hard problems, and hence, metaheuristics optimization algorithms are widely used for this task. Invasive Weed Optimization (IWO) algorithm is relatively novel and succussed in several real-world applications. Our experiments demonstrate that the ...

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to various, mostly continuous, optimization problems. For all such heuristically guided search algorithms balance between exploitation and exploration is the determining factor for success. It is generally considered that in the ABC algorithm exploitation is performed by employed ...

In studies of genetic algorithms, evolutionary computing, and ant colony mechanisms, it is recognized that the higher-order forms of collective intelligence play an important role in metaheuristic computing and computational intelligence. Collective intelligence is an integration of collective behaviors of individuals in social groups or collective functions of components in computational intel...

Nowadays genetic algorithms stand as a trend to solve NPcomplete and NP-hard problems. In this paper, we present a new hybrid metaheuristic which uses Parallel Genetic Algorithms and Scatter Search coupled with a decomposition-into-petals procedure for solving a class of Vehicle Routing and Scheduling Problems. The parallel genetic algorithm presented is based on the island model and was run on...

Travelling Salesman Problem (TSP) is the problem for finding shortest route starting from start node then visiting number of nodes exactly once and finally go back to node. If a TSP has lot nodes, it will be NP-Hard Problem. Algorithms working based on heuristic metaheuristic can solution solving Savings Algorithm algorithm, so it’s may not best solution, therefore there chance improve it. Gene...

This paper presents a new stochastic nature inspired methodology, which is based on the concepts of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), for optimally clustering N objects into K clusters. Due to the nature of stochastic and population-based search, the proposed algorithm can overcome the drawbacks of traditional clustering methods. Its performance is compared wi...

A new metaheuristic method, the BAT search algorithm based on the echolocation behaviour of bats is proposed in this paper for optimal design of Power System Stabilizers (PSSs) in a multimachine environment. The PSSs parameter tuning problem is converted to an optimization problem which is solved by BAT search Algorithm. An eigenvalues based objective function involving the damping factor, and ...