Results 21 to 30 of about 50,815 (253)
Online metaheuristic algorithm selection
The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation.
Kazem Meidani +2 more
openaire +2 more sources
Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimize total tardiness [PDF]
The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the
Fernández-Viagas Escudero, Víctor +2 more
core +1 more source
The Solar System Algorithm: A Novel Metaheuristic Method for Global Optimization
A novel metaheuristic algorithm for global optimization, called the Solar System Algorithm (SSA), is presented. The proposed algorithm imitates the orbiting behavior of some objects found in the solar system: i.e., Sun, planets, moons, stars, and black ...
Farouq Zitouni +2 more
doaj +1 more source
Two-Stage Eagle Strategy with Differential Evolution [PDF]
Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications.
Deb, Suash, Yang, Xin-She
core +1 more source
Overview of Metaheuristic Algorithms
Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem.
Saman M. Almufti +5 more
openaire +1 more source
Firefly Algorithm, Stochastic Test Functions and Design Optimisation [PDF]
Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimization problems. In this paper, we show how to use the recently developed Firefly Algorithm to solve nonlinear design problems.
Yang, Xin-She
core +1 more source
On the use of biased-randomized algorithms for solving non-smooth optimization problems [PDF]
Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory ...
Ferrer Biosca, Albert +4 more
core +3 more sources
Gold Rush Optimizer. A New Population-Based Metaheuristic Algorithm [PDF]
Today's world is characterised by competitive environments, optimal resource utilization, and cost reduction, which has resulted in an increasing role for metaheuristic algorithms in solving complex modern problems. As a result, this paper introduces the
Kamran Zolfi
doaj
Efficiency Analysis of Swarm Intelligence and Randomization Techniques [PDF]
Swarm intelligence has becoming a powerful technique in solving design and scheduling tasks. Metaheuristic algorithms are an integrated part of this paradigm, and particle swarm optimization is often viewed as an important landmark.
Yang, Xin-She
core +1 more source
Trigonometric words ranking model for spam message classification
Abstract The significant increase in the volume of fake (spam) messages has led to an urgent need to develop and implement a robust anti‐spam method. Several of the current anti‐spam systems depend mainly on the word order of the message in determining the spam message, which results in the system's inability to predict the correct type of message when
Suha Mohammed Hadi +7 more
wiley +1 more source

