On the Hybridization of Memetic Algorithms With Branch-and-Bound Techniques

Gallardo, José E.; Cotta, Carlos; Fernández, Antonio J.

doi:10.1109/tsmcb.2006.883266

Cited by 59 publications

(39 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The approach can be used for finding the best solution for different MIPs [39]. Another work proposed by Gallardo et al (2007) in which flow of beam search and memetic algorithm are intertwined [60]. A fruitful problem specific paradigm for large neighborhood search has been proposed by Prandtstetter and Raidl (2008) for solving the car sequencing problem [79].…”

Section: Hybridizing Metaheuristic With Tree Search Techniquementioning

confidence: 99%

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

View full text Add to dashboard Cite

This paper is intended to give a review of metaheuristic and their application to combinatorial optimization problems. This paper comprises a snapshot of the rapid evolution of metaheuristic concepts, their convergence towards a unified framework and the richness of potential application in combinatorial optimization problems. Over the years, combinatorial optimization problems are gaining awareness of the researchers both in scientific as well as industrial world. This paper aims to present a brief survey of different metaheuristic algorithms for solving the combinatorial optimization problems. Basically we have divided the metaheuristic into three broad categories namely trajectory methods, population based methods and hybrid methods. Trajectory methods are those that deal with a single solution. These include simulated annealing, tabu search, variable neighborhood search and greedy randomized adaptive search procedure. Population based methods deal with a set of solutions. These include genetic algorithm, ant colony optimization and particle swarm optimization. Hybrid methods deal with the hybridization of single point search methods and population based methods. These are further categorized into five different types. Finally we conclude the paper by giving some issues which are needed to develop a well performed metaheuristic algorithm.

show abstract

Section: Hybridizing Metaheuristic With Tree Search Techniquementioning

confidence: 99%

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Baghel¹,

Agrawal²,

Silakari³

2012

IJCA

View full text Add to dashboard Cite

show abstract

“…The term MA is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. So far, MAs are extensively used for solving many optimization problems, such as scheduling problems [20], [1], travel salesman problems [27], combinatorial optimization problems [13], quadratic assignment problems [24] and other applications [6], [16].…”

Section: Behaviors Of Agentsmentioning

confidence: 99%

Agent Based Evolutionary Dynamic Optimization

Yang

Wang

et al. 2010

Agent-Based Evolutionary Search

View full text Add to dashboard Cite

Summary Agent-based Evolutionary Search (AES) has attracted a growing interest from the evolutionary computation community in recent years due to its robust ability in solving large scale problems, ranging from online trading, disaster response, modeling social structures to financial investment planning. In order to solve these problems, a great variety of intelligent techniques have been developed to improve the framework and efficiency of AES. This chapter investigates an agent based evolutionary search algorithm in which the agents are updated and co-evolve to track dynamic optimum by imitating the exhibited feature of living organism. In the proposed algorithm, all agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the predefined energy function, individual agent can compete with its neighbors and also can acquire knowledge through cumulative information. For the purpose of maintaining the diversity of the population, random immigrants and adaptive primal dual mapping schemes are incorporated. Simulation experiments on a set of dynamic benchmark problems show the proposed AES algorithm can yield a better performance on dynamic optimization problems (DOPs) in comparison with several peer algorithms.

show abstract

“…Note that Hybrid MA-BS is a current state-of-the-art technique for the SCSP. The results for all three techniques are taken from [9]. The stopping criterion of MM, WMM, and Hybrid MA-BS was 600 CPU time seconds on a Pentium IV PC with 2400 MHz and 512 Mb of memory.…”

Section: Final Experimental Evaluationmentioning

confidence: 99%

A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem

Blum

Cotta

Fernández

et al. 2007

Evolutionary Computation in Combinatorial Optimization

Self Cite

View full text Add to dashboard Cite

The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness.

show abstract

On the Hybridization of Memetic Algorithms With Branch-and-Bound Techniques

Cited by 59 publications

References 24 publications

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Survey of Metaheuristic Algorithms for Combinatorial Optimization

Agent Based Evolutionary Dynamic Optimization

A Probabilistic Beam Search Approach to the Shortest Common Supersequence Problem

Contact Info

Product

Resources

About