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Infeasibility and structural bias in differential evolution

INFORMATION SCIENCES 2019年 496卷 161-179页

作者： Caraffini, Fabio Kononova, Anna V. Corne, David De Montfort Univ Sch Comp Sci & Informat Inst Artificial Intelligence Leicester LE1 9BH Leics England Heriot Watt Univ Dept Comp Sci Edinburgh EH14 4AS Midlothian Scotland Hogesch Rotterdam Sch Commun Media & Informat Technol Rotterdam Netherlands

Structural bias is a recently identified property of optimisation algorithms, causing them to favour certain regions of the search space over others, independently of the objective function. Since structural bias can adversely affect the progress of optimisation, a better understanding of it is needed in order to inform the theory and practice of algorithm design. For example, it is generally accepted that larger populations are favoured when solution quality is paramount and time constraints are permissive. However, common variants of both Genetic algorithms and Particle Swarm Optimisation have been found to exhibit structural bias that increases with population size. Herein we investigate structural bias in popular variants of Differential Evolution (DE), and attempt to identify which algorithm features trigger its emergence. In particular, we focus on the (often overlooked) constraint handling mechanism. Our results suggest that DE is generally robust to structural bias. Only one of the variants studied - DE/current-to-best/1/bin - shows clear signs of bias, however this is mitigated by a judicious choice of constraint handling technique. These findings contribute towards explaining the widespread success of DE in algorithm comparison studies;its robustness to structural bias represents the absence of a factor that may confound other algorithms. (C) 2019 Published by Elsevier Inc.

关键词： Structural bias Algorithmic design Differential evolution population-based algorithms Optimisation

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A Survey on GPU-based Implementation of Swarm Intelligence algorithms

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IEEE TRANSACTIONS ON CYBERNETICS 2016年第9期46卷 2028-2041页

作者： Tan, Ying Ding, Ke Peking Univ Sch Elect Engn & Comp Sci Dept Machine Intelligence Key Lab Machine Percept Beijing 100871 Peoples R China

Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been developed and widely used for solving optimization problems. When applied to complex problems, a large number of fitness function evaluations are needed to obtain an acceptable solution. To tackle this vital issue, graphical processing units (GPUs) have been used to accelerate the optimization procedure of SIAs. Thanks to their inherent parallelism, SIAs are very suitable for parallel implementation under the GPU platform which have achieved a great success in recent years. This paper presents a comprehensive review of GPU-based parallel SIAs in accordance with a newly proposed taxonomy. Critical concerns for the efficient parallel implementation of SIAs are also described in detail. Moreover, novel criteria are also proposed to evaluate and compare the parallel implementation and algorithm performance universally. The rationality and practicability of the proposed optimization methodology and criteria are verified by careful case study. Finally, our opinions and perspectives on the trends and prospects on the relatively new research domain are also presented for future development.

关键词： Compute unified device architecture (CUDA) GPU computing heterogeneous computing open computing language (OpenCL) population-based algorithms swarm intelligence algorithms (SIAs)

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A normalization method for solving the combined economic and emission dispatch problem with meta-heuristic algorithms

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 54卷 163-186页

作者： Liang, Yun-Chia Juarez, Josue Rodolfo Cuevas Yuan Ze Univ Dept Ind Engn & Management Tao Yuan 32003 Taiwan

Solving the economic and emission dispatch (ED/MED) problems separately becomes more complex when the combined version (CEED) of the two aforementioned cases is considered. The basic idea is to achieve the lowest possible cost with the smallest amount of pollutant and this problem is known as the combined economic-emission dispatch (CEED). A new approach for solving the CEED is presented in this paper. The idea consists of normalizing the two conflicting objective functions, ED and MED, using the mean and standard deviation of the members contained in the population-based meta-heuristic algorithms implemented in this study thus preventing units and scale differences when optimizing the CEED problem. The mathematical model for each problem (ED, MED. and CEED) presented in this study is optimized implementing a nonlinear optimization package named TOMLAB available for MATLAB, which helps us to determine the best possible solution for the tested instances. A novel meta-heuristic named Virus Optimization Algorithm (VOA) is implemented along with seven well-known algorithmic tools, which are the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Differential Evolution (DE), FireFly algorithm (FF), Gravitational Search Algorithm (GSA), and Seeker Optimization Algorithm (SOA). A comprehensive statistical study is performed to determine the quality of the solutions delivered by the algorithmic tools when compared with TOMLAB. From the test instances, it was observed that the proposed normalization method does not only show to be feasible, but also helps the algorithms to achieve similar results from that coming when solving the ED and MED separately. Furthermore, among the eight meta-heuristic tools (VOA, GA, PSO, HS, DE, FF, GSA, and SOA), VOA showed outstanding performance in both solution quality and computational efficiency. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Combined economic and emission dispatch problem Meta-heuristics population-based algorithms Nature-inspired algorithms

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Cuckoo search and firefly algorithms in terms of generalized net theory

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SOFT COMPUTING 2020年第7期24卷 4877-4898页

作者： Roeva, Olympia Zoteva, Dafina Atanassova, Vassia Atanassov, Krassimir Castillo, Oscar Bulgarian Acad Sci Inst Biophys & Biomed Engn Sofia Bulgaria Tijuana Inst Technol Tijuana BC Mexico

In the presented paper, the functioning and the results of the work of two metaheuristic algorithms, namely cuckoo search algorithm (CS) and firefly algorithm (FA), are described using the apparatus of generalized nets (GNs), which is an appropriate and efficient tool for describing the essence of various optimization methods. The two developed GN-models mimic the optimization processes based on the nature of cuckoos and fireflies, respectively. The proposed GN-models execute the two considered metaheuristic algorithms conducting basic steps and performing optimal search. Building upon these two GN-models, a universal GN-model is constructed that can be used for describing and simulating both the CS and the FA by setting different characteristic functions of the GN-tokens. Moreover, the universal GN-model itself can be transformed to each of the herewith presented GN-models by applying appropriate hierarchical operators. In order to validate the proposed universal GN-model, numerical experiments are performed for the operating of the universal GN-model (CS and FA) on benchmark mathematical functions. The obtained results are compared with the results of the GNmodel of CS, GN-model of FA, as well as the results of the standard CS and FA.

关键词： Cuckoo search algorithm Firefly algorithm Generalized net Metaheuristic Model population-based algorithms Benchmark functions

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Electric fish optimization: a new heuristic algorithm inspired by electrolocation

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NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11543-11578页

作者： Yilmaz, Selim Sen, Sevil Hacettepe Univ WISE Lab Ankara Turkey

Swarm behaviors in nature have inspired the emergence of many heuristic optimization algorithms. They have attracted much attention, particularly for complex problems, owing to their characteristics of high dimensionality, nondifferentiability, and the like. A new heuristic algorithm is proposed in this study inspired by the prey location and communication behaviors of electric fish. Nocturnal electric fish have very poor eyesight and live in muddy, murky water, where visual senses are very limited. Therefore, they rely on their species-specific ability called electrolocation to perceive their environment. The active and passive electrolocation capability of such fish is believed to be a good candidate for balancing local and global search, and hence it is modeled in this study. A new heuristic called electric fish optimization (EFO) is introduced and compared with six well-known heuristics (simulated annealing, SA;vortex search, VS;genetic algorithm, GA;differential evolution, DE;particle swarm optimization, PSO;and artificial bee colony, ABC). In the experiments, 50 basic and 30 complex mathematical functions, 13 clustering problems, and five real-world design problems are used as the benchmark sets. The simulation results indicate that EFO is better than or very competitive with its competitors.

关键词： Nature-inspired algorithm Heuristics Swarm intelligence Real parameter optimization Single-solution algorithms population-based algorithms Real-world applications

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On the Benefits of populations for the Exploitation Speed of Standard Steady-State Genetic algorithms

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ALGORITHMICA 2020年第12期82卷 3676-3706页

作者： Corus, Dogan Oliveto, Pietro S. Univ Sheffield Sheffield S Yorkshire England

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard (mu + 1) GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to mu = o(root log n). Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: (1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. (2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.

关键词： Genetic algorithms Crossover Recombination population-based algorithms Theory Runtime analysis

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Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第3期14卷 1078-1088页

作者： Lapa, Krystian Cpalka, Krzysztof Czestochowa Tech Univ Inst Computat Intelligence PL-42200 Czestochowa Poland

In this paper, a new approach to fuzzy controller construction which combines features of proportional-integral-derivative controllers and flexible fuzzy systems is proposed. Flexibility of the fuzzy systems used makes it possible to interpret a control process and reduce the controller structure, which can make hardware implementation easier. The proposed approach uses knowledge about the controlled object and possibilities offered by nature-inspired hybrid population-based algorithms. These algorithms allow us to simultaneously select controller structure and parameters while taking into account a set of evaluation criteria. The criteria can include the quality of control, controller complexity, control process interpretability, etc. This is important in those applications where an atypical controller structure needs to be selected or an extended set of expectations has to be taken into account. This approach was tested using typical control problems.

关键词： Fuzzy control nature-inspired methods proportional-integral-derivative (PID) controller population-based algorithms

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An evolutionary algorithm-based approach to robust analog circuit design using constrained multi-objective optimization

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KNOWLEDGE-based SYSTEMS 2008年第3期21卷 175-183页

作者： Nicosia, Giuseppe Rinaudo, Salvatore Sciacca, Eva Univ Catania Dept Math & Comp Sci I-95125 Catania Italy ST Microelect Catania Italy

The increasing complexity of circuit design needs to be managed with appropriate optimization algorithms and accurate statistical description of design models in order to reach the design specifics, guaranteeing "zero defects". In the Design for Yield open problems are the design of effective optimization algorithms and statistical analysis for yield design, which require time consuming techniques. New methods have to balance accuracy, robustness and computational effort. Typical analog integrated circuit optimization problems are computationally hard and require the handling of multiple, conflicting, and non-commensurate objectives having strong nonlinear interdependence. This paper tackles the problem by evolutionary algorithms to produce tradeoff solutions on the Pareto Front. In this research work Integrated Circuit (IC) design has been formulated as a constrained multi-objective optimization problem defined in a mixed integer/discrete/continuous domain. The following real-life circuits, RF Low Noise Amplifier, Leapfrog Filter, and Ultra Wideband LNA, were selected as test bed. The proposed algorithm, A-NSGAII, was shown to produce acceptable and robust solutions in the tested applications, where state-of-art algorithms and circuit designers failed. The results show significant improvement in all the chosen IC design problems. (c) 2008 Published by Elsevier B.V.

关键词： analog circuit design circuit sizing design for yield design for manufacturability genetic algorithms population-based algorithms evolutionary optimization multi-objective optimization constrained optimization

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A new optimization algorithm based on mimicking the voting process for leader selection

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PEERJ COMPUTER SCIENCE 2022年 8卷 e976页

作者： Trojovsky, Pavel Dehghani, Mohammad Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove Czech Republic

Stochastic-based optimization algorithms are effective approaches to addressing optimization challenges. In this article, a new optimization algorithm called the Election-based Optimization Algorithm (EBOA) was developed that mimics the voting process to select the leader. The fundamental inspiration of EBOA was the voting process, the selection of the leader, and the impact of the public awareness level on the selection of the leader. The EBOA population is guided by the search space under the guidance of the elected leader. EBOA's process is mathematically modeled in two phases: exploration and exploitation. The efficiency of EBOA has been investigated in solving thirty-three objective functions of a variety of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and CEC 2019 types. The implementation results of the EBOA on the objective functions show its high exploration ability in global search, its exploitation ability in local search, as well as the ability to strike the proper balance between global search and local search, which has led to the effective efficiency of the proposed EBOA approach in optimizing and providing appropriate solutions. Our analysis shows that EBOA provides an appropriate balance between exploration and exploitation and, therefore, has better and more competitive performance than the ten other algorithms to which it was compared.

关键词： Optimization Optimization problem Human-based metahurestic algorithm Stochastic algorithms population-based algorithms Applied mathematics Voting process Leader selection population matrix Recurring process

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Modified biogeography-based optimisation (MBBO)

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第4期3卷 252-266页

作者： Lohokare, M. R. Devi, S. Pattnaik, S. S. Panigrahi, B. K. Joshi, J. G. Natl Inst Tech Teachers Training & Res Dept Elect & Commun Chandigarh 160019 India Natl Inst Tech Teachers Training & Res Educ Televis Ctr Chandigarh 160019 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

Modified biogeography-based optimisation (MBBO) technique is modified version of BBO which is a bio-inspired and population-based optimisation technique. Biogeography is the study of distribution of species in nature. In this paper, BBO is modified by applying the concept of mutation as extended migration in phenomenon of mutation and prevention of similar habitats. Mutation as concept of extended migration is based on sharing of the information from best solution which controls the diversity among the population limited to the near feasible solution. These make the fine-tuning of the algorithm from one generation to next and converge fast. Fourteen standard benchmark functions are used to demonstrate the performance of MBBO. The results are compared with eight other population-based optimisation algorithms with discrete version of population. The experimental results are analysed using statistical two paired t-test. MBBO performance is also compared with other modified BBO algorithms. To show the capability of MBBO to solve the problems of continuous type, MBBO and other optimisation algorithms are applied as soil computing tool to calculate the resonant frequency of rectangular microstrip patch antennas of different dimensions having various substrate thicknesses.

关键词： modified biogeography-based optimisation MBBO biogeography population-based algorithms resonant frequency rectangular microstrip patch antenna RMSA

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