133 research outputs found
A PAPR Reduction for OFDM Signals Based on Self-Adaptive Multipopulation DE algorithm
One of major drawbacks of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). A signal with high PAPR leads to nonlinear distortion caused mainly by power amplifiers in wireless transmitters. Partial transmit sequence (PTS) is one of the most attractive methods to reduce the PAPR in OFDM systems. It achieves considerable PAPR reduction without distortion, but it requires an exhaustive search over all the combinations of the given phase factors, which results in a computational complexity that increases exponentially with the number of partitions. For this optimization problem, we propose in this paper a suboptimal PTS method based on the self-adaptive multipopulation differential evolution algorithm (SAMDE). The self adaptation of control parameters and structured population, is able to obtain high quality solutions with low computational cost by evolving each sub-population of individuals over successive generations
The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems
Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others
Diversity Control in Evolutionary Computation using Asynchronous Dual-Populations with Search Space Partitioning
Diversity control is vital for effective global optimization using evolutionary computation (EC) techniques. This paper classifies the various diversity control policies in the EC literature. Many research works have attributed the high risk of premature convergence to sub-optimal solutions to the poor exploration capabilities resulting from diversity collapse. Also, excessive cost of convergence to optimal solution has been linked to the poor exploitation capabilities necessary to focus the search. To address this exploration-exploitation trade-off, this paper deploys diversity control policies that ensure sustained exploration of the search space without compromising effective exploitation of its promising regions. First, a dual-pool EC algorithm that facilitates a temporal evolution-diversification strategy is proposed. Then a quasi-random heuristic initialisation based on search space partitioning (SSP) is introduced to ensure uniform sampling of the initial search space. Second, for the diversity measurement, a robust convergence detection mechanism that combines a spatial diversity measure; and a population evolvability measure is utilised. It was found that the proposed algorithm needed a pool size of only 50 samples to converge to optimal solutions of a variety of global optimization benchmarks. Overall, the proposed algorithm yields a 33.34% reduction in the cost incurred by a standard EC algorithm. The outcome justifies the efficacy of effective diversity control on solving complex global optimization landscapes.
Keywords: Diversity, exploration-exploitation tradeoff, evolutionary algorithms, heuristic initialisation, taxonomy
Adaptive distributed differential evolution
Due to the increasing complexity of optimization problems, distributed differential evolution (DDE) has become a promising approach for global optimization. However, similar to the centralized algorithms, DDE also faces the difficulty of strategies' selection and parameters' setting. To deal with such problems effectively, this article proposes an adaptive DDE (ADDE) to relieve the sensitivity of strategies and parameters. In ADDE, three populations called exploration population, exploitation population, and balance population are co-evolved concurrently by using the master-slave multipopulation distributed framework. Different populations will adaptively choose their suitable mutation strategies based on the evolutionary state estimation to make full use of the feedback information from both individuals and the whole corresponding population. Besides, the historical successful experience and best solution improvement are collected and used to adaptively update the individual parameters (amplification factor F and crossover rate CR) and population parameter (population size N), respectively. The performance of ADDE is evaluated on all 30 widely used benchmark functions from the CEC 2014 test suite and all 22 widely used real-world application problems from the CEC 2011 test suite. The experimental results show that ADDE has great superiority compared with the other state-of-the-art DDE and adaptive differential evolution variants
Fast micro-differential evolution for topological active net optimization
This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a ``best improvement local search'' (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm
A Novel Black Box Process Quality Optimization Approach based on Hit Rate
Hit rate is a key performance metric in predicting process product quality in
integrated industrial processes. It represents the percentage of products
accepted by downstream processes within a controlled range of quality. However,
optimizing hit rate is a non-convex and challenging problem. To address this
issue, we propose a data-driven quasi-convex approach that combines factorial
hidden Markov models, multitask elastic net, and quasi-convex optimization. Our
approach converts the original non-convex problem into a set of convex feasible
problems, achieving an optimal hit rate. We verify the convex optimization
property and quasi-convex frontier through Monte Carlo simulations and
real-world experiments in steel production. Results demonstrate that our
approach outperforms classical models, improving hit rates by at least 41.11%
and 31.01% on two real datasets. Furthermore, the quasi-convex frontier
provides a reference explanation and visualization for the deterioration of
solutions obtained by conventional models
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Ecological theatre and the evolutionary game: how environmental and demographic factors determine payoffs in evolutionary games
In the standard approach to evolutionary games and replicator dynamics, differences in fitness can be interpreted as an excess from the mean Malthusian growth rate in the population. In the underlying reasoning, related to an analysis of "costs" and "benefits", there is a silent assumption that fitness can be described in some type of units. However, in most cases these units of measure are not explicitly specified. Then the question arises: are these theories testable? How can we measure "benefit" or "cost"? A natural language, useful for describing and justifying comparisons of strategic "cost" versus "benefits", is the terminology of demography, because the basic events that shape the outcome of natural selection are births and deaths. In this paper, we present the consequences of an explicit analysis of births and deaths in an evolutionary game theoretic framework. We will investigate different types of mortality pressures, their combinations and the possibility of trade-offs between mortality and fertility. We will show that within this new approach it is possible to model how strictly ecological factors such as density dependence and additive background fitness, which seem neutral in classical theory, can affect the outcomes of the game. We consider the example of the Hawk-Dove game, and show that when reformulated in terms of our new approach new details and new biological predictions are produced
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
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