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Found 38 papers, 5 papers with code
Review of Parameter Tuning Methods for Nature-Inspired Algorithms
Almost all optimization algorithms have algorithm-dependent parameters, and the setting of such parameter values can largely influence the behaviour of the algorithm under consideration.
Ten New Benchmarks for Optimization
Benchmarks are used for testing new optimization algorithms and their variants to evaluate their performance.
A Nature-Inspired Feature Selection Approach based on Hypercomplex Information
The essential idea behind it is to find the most suitable subset of features according to some criterion.
COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking
In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand.
Nature-Inspired Optimization Algorithms: Challenges and Open Problems
Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints.
Neighborhood Information-based Probabilistic Algorithm for Network Disintegration
Network structure and integrity can be controlled by a set of key nodes, and to find the optimal combination of nodes in a network to ensure network structure and integrity can be an NP-complete problem.
Influence of Initialization on the Performance of Metaheuristic Optimizers
Differential evolution depends more heavily on the number of iterations, a relatively small population with more iterations can lead to better results.
The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory
In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria.
Multi-Species Cuckoo Search Algorithm for Global Optimization
Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems.
Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team
This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively.
Social Algorithms
This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms.
New directional bat algorithm for continuous optimization problems
To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities.
Why the Firefly Algorithm Works?
Firefly algorithm is a nature-inspired optimization algorithm and there have been significant developments since its appearance about ten years ago.
Cuckoo Search: State-of-the-Art and Opportunities
Since the development of cuckoo search (CS) by Yang and Deb in 2009, CS has been applied in a diverse range of applications.
Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach
Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed.
Swarm Intelligence: Past, Present and Future
Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems.
LibOPT: An Open-Source Platform for Fast Prototyping Soft Optimization Techniques
Optimization techniques play an important role in several scientific and real-world applications, thus becoming of great interest for the community.
Random-Key Cuckoo Search for the Travelling Salesman Problem
Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve.
A Discrete Firefly Algorithm to Solve a Rich Vehicle Routing Problem Modelling a Newspaper Distribution System with Recycling Policy
This is the first study of such a problem in the literature.
An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats.
Cuckoo Search: Recent Advances and Applications
Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and CS is efficient in solving global optimization problems.
Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately.
Bat Algorithm is Better Than Intermittent Search Strategy
The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization
All swarm-intelligence-based optimization algorithms use some stochastic components to increase the diversity of solutions during the search process.
Computational Optimization, Modelling and Simulation: Recent Trends and Challenges
This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.
Multi-objective Flower Algorithm for Optimization
Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants.
True Global Optimality of the Pressure Vessel Design Problem: A Benchmark for Bio-Inspired Optimisation Algorithms
The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimization algorithms.
Bio-Inspired Computation: Success and Challenges of IJBIC
It is now five years since the launch of the International Journal of Bio-Inspired Computation (IJBIC).
Swarm Intelligence Based Algorithms: A Critical Analysis
Many optimization algorithms have been developed by drawing inspiration from swarm intelligence (SI).
A Physarum-Inspired Approach to Optimal Supply Chain Network Design at Minimum Total Cost with Demand Satisfaction
A supply chain is a system which moves products from a supplier to customers.
A comprehensive review of firefly algorithms
This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice.
Flower Pollination Algorithm for Global Optimization
Flower pollination is an intriguing process in the natural world.
A Literature Survey of Benchmark Functions For Global Optimization Problems
Test functions are important to validate and compare the performance of optimization algorithms.
Bat Algorithm: Literature Review and Applications
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient.
Firefly Algorithm: Recent Advances and Applications
Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years.
A Brief Review of Nature-Inspired Algorithms for Optimization
Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature.
A hybrid bat algorithm
Swarm intelligence is a very powerful technique to be used for optimization purposes.
Cuckoo Search via Levy Flights
In this paper, we intend to formulate a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems.
Optimization and Control optimization and control
