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General Introduction
Published in Yong-Zai Lu, Yu-Wang Chen, Min-Rong Chen, Peng Chen, Guo-Qiang Zeng, Extremal Optimization, 2018
Yong-Zai Lu, Yu-Wang Chen, Min-Rong Chen, Peng Chen, Guo-Qiang Chen
To build the bridge between statistical physics and computational complexity and find high-quality solutions for hard optimization problems, an extremal dynamics-oriented local-search heuristic, the so-called extremal optimization (EO) has been proposed (Boettcher and Percus, 1999). EO was originally developed from the fundamental of statistical physics. More specifically, EO is inspired by self-organized criticality (SOC) (Bak et al., 1987), which is a statistical physics concept to describe a class of systems that have a critical point as an attractor. Moreover, as also indicated by Bak et al. (1987), the concept of SOC may be further described and experimented on by a sandpile model as shown in Figure 1.2. If a sandpile is formed on a horizontal circular base with any arbitrary initial distribution of sand grains, if a steady state of the sandpile is formed by slowly adding sand grains gradually, the surface of the sandpile makes on average a constant angle with the horizontal plane. The addition of each sand grain results in some activity on the surface of the pile: an avalanche of sand mass follows, which propagates on the surface of the sandpile. In the stationary regime, avalanches are of many different sizes that they would have a power law distribution. If one starts with an initial uncritical state, initially most of the avalanches are small, but the range of sizes of avalanches grows with time. After a long time, the system arrives at a critical state, in which the avalanches extend overall length and time scales.
Community Detection Techniques and Metrics: A State-of-the-Art Survey
Published in Rajesh Singh, Anita Gehlot, P.S. Ranjit, Dolly Sharma, Futuristic Sustainable Energy and Technology, 2022
Chaitali Choudhary, Inder Singh
Extremal Optimization: A method which progressively replaces very unpleasant variables in an unsatisfactory solution with new, random ones is referred to as extremal optimization. Although extreme optimization is primarily used to find non-overlapping communities, D. Jin et al. [10] developed an EO-based technique for optimizing the modularity function of networks and detecting their overlapping communities.
Fractional Order PID Design based on Novel Improved Slime Mould Algorithm
Published in Electric Power Components and Systems, 2021
Davut Izci, Serdar Ekinci, H. Lale Zeynelgil, John Hedley
Direct current (DC) motor and automatic voltage regulator (AVR) systems have been application areas for many meta-heuristic algorithms as a simple second order and higher order complex systems, respectively. Both systems are being adopted widely as real-world engineering problems for assessing meta-heuristic algorithms since they provide an observable test bed for performance evaluations and comparisons. Some of the examples of metaheuristic optimization methods for DC motor control can be listed as Henry gas solubility optimization (HGSO) [3], Harris hawks optimization (HHO) [4], gray wolf optimization (GWO) [2], atom search optimization (ASO) [5], genetic algorithm (GA) [6,7], ant colony optimization (ACO) [8], swarm learning process (SLP) [1], stochastic fractal search (SFS) [9], flower pollination (FPA) [10], particle swarm optimization (PSO) [7,11], and teaching-learning-based optimization (TLBO) [12] algorithms. Likewise, examples of meta-heuristic optimization methods for AVR control system are also available such as salp swarm optimization (SSA) [13], jaya optimization (JOA) [14], sine-cosine (SCA) [15], tree seed (TSA) [16], yellow saddle goatfish (YSGA) [17], HGSO [18], cuckoo search (CS) [19], SFS [20], water cycle (WCA) [21], crow search (CSA) [22], and artificial ecosystem-based optimization (AEO) [23] along with improved versions of kidney-inspired (IKA) [24] and multi-objective extremal optimization (MOEO) [25] algorithms.
An improved constrained multi-objective optimization evolutionary algorithm for carbon fibre drawing process
Published in Systems Science & Control Engineering, 2019
The multi-objective optimization problem is common in the practical engineering area such as knapsack problems (Ponsich, Jaimes, & Coello, 2013), power system frequency control (Wang et al., 2017), carbon fibre drawing process (Chen, Ding, Jin, & Hao, 2013), and so on. It is well known that the evolutionary algorithm is an effective tool to solve the multi-objective optimization problem and, in the past decades, a variety of evolutionary algorithms have been proposed/developed such as genetic algorithm (Deb, Pratap, Agarwal, & Meyarivan, 2002), differential evolution (DE) algorithm (Bandyopadhyay & Mukherjee, 2015; Wang, Liao, Zhou, & Cai, 2014), extremal optimization algorithm (Li, Lu, Zeng, Wu, & Chen, 2016; Lu, Zhou, Zeng, & Du, 2018), PSO algorithm (Zeng et al., 2018; Zeng, Wang, & Zhang, 2016; Zeng, Wang, Zhang, & Alsaadi, 2016; Zeng, Zhang, Liu, Liang, & Alsaadi, 2017), and immune algorithm (Khaleghi, Farsangi, Nezamabadi-Pour, & Lee, 2011). Note that the evolutionary algorithms mentioned above are only able to deal with the multi-objective optimization problems without constraints or with box-constraints. However, in most practical engineering applications, the objective functions to be optimized are accompanied with the complex equality or inequality constraints. Therefore, it is of practical significance to find a suitable constraint handling technology (CHT) so as to obtain an optimal solution to the constrained multi-objective problem (CMOP).
Empirical validation of vehicle type-dependent car-following heterogeneity from micro- and macro-viewpoints
Published in Transportmetrica B: Transport Dynamics, 2019
Liang Zheng, Chuang Zhu, Zhengbing He, Tian He, Sisi Liu
Here, in the outer loop, one heuristic algorithm named monkey algorithm (MA) with dynamic adaptation (DAMA) proposed by Zheng (2013), with strong global optimization capability for the multi-extremal optimization problem, is employed to search for the optimal model parameters. For more introductions about MA and DAMA, please refer to the Appendix.