960 research outputs found
Memetic firefly algorithm for combinatorial optimization
Firefly algorithms belong to modern meta-heuristic algorithms inspired by
nature that can be successfully applied to continuous optimization problems. In
this paper, we have been applied the firefly algorithm, hybridized with local
search heuristic, to combinatorial optimization problems, where we use graph
3-coloring problems as test benchmarks. The results of the proposed memetic
firefly algorithm (MFFA) were compared with the results of the Hybrid
Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW
method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs
with 500 vertices) generated using the Culberson random graph generator. The
results of firefly algorithm were very promising and showed a potential that
this algorithm could successfully be applied in near future to the other
combinatorial optimization problems as well.Comment: 14 pages; Bioinspired Optimization Methods and their Applications
(BIOMA 2012
Multi-Strategy Coevolving Aging Particle Optimization
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel
population-based algorithm for black-box optimization. In a memetic fashion,
MS-CAP combines two components with complementary algorithm logics. In the
first stage, each particle is perturbed independently along each dimension with
a progressively shrinking (decaying) radius, and attracted towards the current
best solution with an increasing force. In the second phase, the particles are
mutated and recombined according to a multi-strategy approach in the fashion of
the ensemble of mutation strategies in Differential Evolution. The proposed
algorithm is tested, at different dimensionalities, on two complete black-box
optimization benchmarks proposed at the Congress on Evolutionary Computation
2010 and 2013. To demonstrate the applicability of the approach, we also test
MS-CAP to train a Feedforward Neural Network modelling the kinematics of an
8-link robot manipulator. The numerical results show that MS-CAP, for the
setting considered in this study, tends to outperform the state-of-the-art
optimization algorithms on a large set of problems, thus resulting in a robust
and versatile optimizer
A PSO and Pattern Search based Memetic Algorithm for SVMs Parameters Optimization
Addressing the issue of SVMs parameters optimization, this study proposes an
efficient memetic algorithm based on Particle Swarm Optimization algorithm
(PSO) and Pattern Search (PS). In the proposed memetic algorithm, PSO is
responsible for exploration of the search space and the detection of the
potential regions with optimum solutions, while pattern search (PS) is used to
produce an effective exploitation on the potential regions obtained by PSO.
Moreover, a novel probabilistic selection strategy is proposed to select the
appropriate individuals among the current population to undergo local
refinement, keeping a well balance between exploration and exploitation.
Experimental results confirm that the local refinement with PS and our proposed
selection strategy are effective, and finally demonstrate effectiveness and
robustness of the proposed PSO-PS based MA for SVMs parameters optimization.Comment: 27 pages. Neurocomputing, 201
A particle swarm optimization based memetic algorithm for dynamic optimization problems
Copyright @ Springer Science + Business Media B.V. 2010.Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant No. 70431003 and Grant No. 70671020, the National Innovation Research Community Science Foundation of China under
Grant No. 60521003, the National Support Plan of China under Grant No. 2006BAH02A09 and the Ministry of Education, science, and Technology in Korea through the Second-Phase of Brain Korea 21 Project in 2009, the Engineering and Physical Sciences Research
Council (EPSRC) of UK under Grant EP/E060722/01 and the Hong Kong Polytechnic University Research Grants under Grant G-YH60
Survival of the flexible: explaining the recent dominance of nature-inspired optimization within a rapidly evolving world
Although researchers often comment on the rising popularity of
nature-inspired meta-heuristics (NIM), there has been a paucity of data to
directly support the claim that NIM are growing in prominence compared to other
optimization techniques. This study presents evidence that the use of NIM is
not only growing, but indeed appears to have surpassed mathematical
optimization techniques (MOT) in several important metrics related to academic
research activity (publication frequency) and commercial activity (patenting
frequency). Motivated by these findings, this article discusses some of the
possible origins of this growing popularity. I review different explanations
for NIM popularity and discuss why some of these arguments remain unsatisfying.
I argue that a compelling and comprehensive explanation should directly account
for the manner in which most NIM success has actually been achieved, e.g.
through hybridization and customization to different problem environments. By
taking a problem lifecycle perspective, this paper offers a fresh look at the
hypothesis that nature-inspired meta-heuristics derive much of their utility
from being flexible. I discuss global trends within the business environments
where optimization algorithms are applied and I speculate that highly flexible
algorithm frameworks could become increasingly popular within our diverse and
rapidly changing world
Experimental Analysis of Design Elements of Scalarizing Functions-based Multiobjective Evolutionary Algorithms
In this paper we systematically study the importance, i.e., the influence on
performance, of the main design elements that differentiate scalarizing
functions-based multiobjective evolutionary algorithms (MOEAs). This class of
MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective
Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very
successful in multiple computational experiments and practical applications.
The two algorithms share the same common structure and differ only in two main
aspects. Using three different multiobjective combinatorial optimization
problems, i.e., the multiobjective symmetric traveling salesperson problem, the
traveling salesperson problem with profits, and the multiobjective set covering
problem, we show that the main differentiating design element is the mechanism
for parent selection, while the selection of weight vectors, either random or
uniformly distributed, is practically negligible if the number of uniform
weight vectors is sufficiently large
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes an adaptive surrogate model assisted CC framework.
This framework adaptively constructs surrogate models for different
sub-problems by fully considering their characteristics. For the single
dimensional sub-problems obtained through decomposition, accurate enough
surrogate models can be obtained and used to find out the optimal solutions of
the corresponding sub-problems directly. As for the nonseparable sub-problems,
the surrogate models are employed to evaluate the corresponding sub-solutions,
and the original simulation model is only adopted to reevaluate some good
sub-solutions selected by surrogate models. By these means, the computation
cost could be greatly reduced without significantly sacrificing evaluation
quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the
concrete algorithm based on this framework is able to find much better
solutions than the conventional CC algorithms and a non-CC algorithm even with
much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974
Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO
This paper presents a comparative analysis of the performance of the
Incremental Ant Colony algorithm for continuous optimization
(), with different algorithms provided in the NLopt library.
The key objective is to understand how the various algorithms in the NLopt
library perform in combination with the Multi Trajectory Local Search (Mtsls1)
technique. A hybrid approach has been introduced in the local search strategy
by the use of a parameter which allows for probabilistic selection between
Mtsls1 and a NLopt algorithm. In case of stagnation, the algorithm switch is
made based on the algorithm being used in the previous iteration. The paper
presents an exhaustive comparison on the performance of these approaches on
Soft Computing (SOCO) and Congress on Evolutionary Computation (CEC) 2014
benchmarks. For both benchmarks, we conclude that the best performing algorithm
is a hybrid variant of Mtsls1 with BFGS for local search.Comment: 24 pages, 10 figure
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Distributed optimization in wireless sensor networks: an island-model framework
Wireless Sensor Networks (WSNs) is an emerging technology in several
application domains, ranging from urban surveillance to environmental and
structural monitoring. Computational Intelligence (CI) techniques are
particularly suitable for enhancing these systems. However, when embedding CI
into wireless sensors, severe hardware limitations must be taken into account.
In this paper we investigate the possibility to perform an online, distributed
optimization process within a WSN. Such a system might be used, for example, to
implement advanced network features like distributed modelling, self-optimizing
protocols, and anomaly detection, to name a few. The proposed approach, called
DOWSN (Distributed Optimization for WSN) is an island-model infrastructure in
which each node executes a simple, computationally cheap (both in terms of CPU
and memory) optimization algorithm, and shares promising solutions with its
neighbors. We perform extensive tests of different DOWSN configurations on a
benchmark made up of continuous optimization problems; we analyze the influence
of the network parameters (number of nodes, inter-node communication period and
probability of accepting incoming solutions) on the optimization performance.
Finally, we profile energy and memory consumption of DOWSN to show the
efficient usage of the limited hardware resources available on the sensor
nodes
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