Preserve Population Diversity Research Articles

A novel hybrid adaptive differential evolution for global optimization

Differential Evolution (DE) stands as a potent global optimization algorithm, renowned for its application in addressing a myriad of practical engineering issues. The efficacy of DE is profoundly influenced by its control parameters and mutation strategies. In light of this, we introduce a refined DE algorithm characterized by adaptive parameters and dual mutation strategies (APDSDE). APDSDE inaugurates an adaptive switching mechanism that alternates between two innovative mutation strategies: DE/current-to-pBest-w/1 and DE/current-to-Amean-w/1. Furthermore, a novel parameter adaptation technique rooted in cosine similarity is established, with the derivation of explicit calculation formulas for both the scaling factor weight and crossover rate weight. In pursuit of optimizing convergence speed whilst preserving population diversity, a sophisticated nonlinear population size reduction method is proposed. The robustness of each algorithm is rigorously evaluated against the CEC2017 benchmark functions, with empirical evidence underscoring the superior performance of APDSDE in comparison to a host of advanced DE variants.

Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm

This study presents a powerful and new modeling variant of bio-inspired algorithms, namely, the Ivy algorithm (IVYA), drawn from the growth patterns of Ivy plants. The algorithm simulates the coordinated and ordered population growth and the spreading and evolution of Ivy plants. The growth rate of Ivy plants is modeled using a differential equation and a data-intensive experimental process. The algorithm utilizes the knowledge of nearby Ivy plants to determine the direction of growth. Additionally, the algorithm mimics the behavior of Ivy plants in nature by choosing the closest and most vital neighbor for self-improvement. The IVYA's unique characteristics of preserving population diversity and its simplicity and flexibility allow for easy modification and extension, thus enabling researchers and practitioners to explore various modifications and techniques to enhance its performance and capabilities. These are the basic needs in optimizing engineering problems. The IVYA is compared with ten other algorithms on 26 classical test functions, demonstrating superior performance. Furthermore, the effectiveness of IVYA is shown by solving 12 engineering optimization problems and comparing the results with various optimization algorithms. The experimental results highlight the efficacy and competitiveness of the IVYA in solving optimization problems. Consider that the source code of the IVYA is publicly available at https://www.optim-app.com/projects/ivya.

Differential evolution with proration-based mutation strategy and multi-segment mixed parameter setting for numerical optimization

In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically selects promising individuals from population as guiders and proportionately allocates the individuals following them based on their fitness values and unsuccessful updates. Besides, we design a multi-segment mixed parameter setting to provide suitable parameters for individuals, considering feedback information from the population, individual requirements, and parameters' interaction. Additionally, we devise an adaptive population maintaining mechanism to refresh the population by replacing individuals with higher unsuccessful updates and removing those with poor fitness values. Unlike previous DE versions, our new algorithm dynamically and appropriately explores promising areas, refines parameter suitability, and accelerates convergence by updating and removing unpromising individuals. This enhances the algorithm search efficiency and achieves a good balance between exploration and exploitation. Finally, we evaluate the proposed algorithm's performance by comparing it with 17 typical or state-of-the-art algorithms on IEEE CEC2014 and CEC2017 test suites. Experimental results indicate that the proposed algorithm is a more promising optimizer.

Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization

Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.

Improved artificial bee colony algorithm based on two-dimensional queue structure for complex optimization problems

In the face of increasingly complex optimization problems, the Artificial Bee Colony algorithm still encounters some unresolved issues. For instance, the randomness involved in generating the initial population provides diversity to optimization solutions but also leads to convergence among individuals due to the current pseudo-randomness in this process. To address this challenge, this study introduces an improved ABC algorithm based on a two-dimensional queue structure, referred to as KLABC. The KLABC algorithm introduces several key advantages over traditional ABC algorithms. By employing the K-Means algorithm, it enhances the assessment of initial population individuals, ensuring a higher-quality population that aligns with specified requirements. The integration of a two-dimensional queue-based search strategy, along with the application of the first-in, first-out (FIFO) mechanism, not only preserves population diversity but also minimizes the risk of discarding potential optimal solutions during iterative searches. Augmenting this framework, the concepts of the global best solution (GBest) and queue best solution (QBest) are incorporated to fortify the exploration formula, expediting the algorithm's convergence rate. Experimental results on clustering optimization problems across seven UCI datasets validate the effectiveness and robustness of the proposed algorithm. Experimental results indicate that the KLABC algorithm achieves faster convergence compared to the traditional ABC algorithm, with an average performance improvement of 55.63%. Additionally, when comparing the runtime of different algorithms in handling large-scale data, the KLABC algorithm effectively maintains its performance even in scenarios with larger data sizes.

Considering spatiotemporal evolutionary information in dynamic multi‐objective optimisation

AbstractPreserving population diversity and providing knowledge, which are two core tasks in the dynamic multi‐objective optimisation (DMO), are challenging since the sampling space is time‐ and space‐varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding‐time‐window‐based population clustering method (SPC) is proposed to effectively solve dynamic multi‐objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM‐MEDA and is compared with other recently proposed state‐of‐the‐art dynamic multi‐objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM‐MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi‐objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point‐based transfer learning method in terms of both computational cost and tracking performance.

Improved Multi-Strategy Matrix Particle Swarm Optimization for DNA Sequence Design

The efficiency of DNA computation is closely related to the design of DNA coding sequences. For the purpose of obtaining superior DNA coding sequences, it is necessary to choose suitable DNA constraints to prevent potential conflicting interactions in different DNA sequences and to ensure the reliability of DNA sequences. An improved matrix particle swarm optimization algorithm, referred to as IMPSO, is proposed in this paper to optimize DNA sequence design. In addition, this paper incorporates centroid opposition-based learning to fully preserve population diversity and develops and adapts a dynamic update on the basis of signal-to-noise ratio distance to search for high-quality solutions in a sufficiently intelligent manner. The results show that the proposal of this paper achieves satisfactory results and can obtain higher computational efficiency.

Multi-Strategy Learning Boosted Colony Predation Algorithm for Photovoltaic Model Parameter Identification

Modeling solar systems necessitates the effective identification of unknown and variable photovoltaic parameters. To efficiently convert solar energy into electricity, these parameters must be precise. The research introduces the multi-strategy learning boosted colony predation algorithm (MLCPA) for optimizing photovoltaic parameters and boosting the efficiency of solar power conversion. In MLCPA, opposition-based learning can be used to investigate each individual's opposing position, thereby accelerating convergence and preserving population diversity. Level-based learning categorizes individuals according to their fitness levels and treats them differently, allowing for a more optimal balance between variation and intensity during optimization. On a variety of benchmark functions, the MLCPA's performance is compared to the performance of the best algorithms currently in use. On a variety of benchmark functions, the MLCPA's performance is compared to that of existing methods. MLCPA is then used to estimate the parameters of the single, double, and photovoltaic modules. Last but not least, the stability of the proposed MLCPA algorithm is evaluated on the datasheets of many manufacturers at varying temperatures and irradiance levels. Statistics have demonstrated that the MLCPA is more precise and dependable in predicting photovoltaic mode critical parameters, making it a viable tool for solar system parameter identification issues.

Elite-ordinary synergistic particle swarm optimization

In particle swarm optimization (PSO), the canonical learning exemplars and information sharing mechanism are often criticized due to the loss of population diversity. Aiming at preserving population diversity and promoting search ability of PSO, this paper introduces an elite-ordinary synergistic particle swarm optimization (EOPSO). In EOPSO, particles are divided into elite and ordinary members based on their fitness performance. Each elite individual learns from itself to maintain population diversity and achieve high-level global exploration. The ordinary ones fly toward a unified target and carry out some assistant local exploitation. In addition, an information interaction based jump-out strategy is designed to overcome particles stagnation situations. The benchmark functions in CEC2017 are employed to compare the performance between the proposed EOPSO with 18 optimization methods (8 state-of-the-art PSO variants and 10 recently proposed non-PSO methods). Experimental comparisons demonstrate that, in EOPSO, the particles have the abilities to reasonably adjust population diversity, effectively avoid local optima, and accurately converge to the global optimum.

An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming.

Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian Genetic Programming (CGP). This article studies four hypotheses on the role of structural introns: noneffective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3) allow neutral search, where structural introns increase the number of neutral mutations and improve performance, and (4) serve as genetic material to enable program growth. We study different variants of LGP controlling the influence of introns for symbolic regression, classification, and digital circuits problems. We find that there is (1) evolved information in the noneffective instructions that can be reactivated and that (2) structural introns can promote programs with higher effective diversity. However, both effects have no influence on LGP search performance. On the other hand, allowing mutations to not only be applied to effective but also to noneffective instructions (3) increases the rate of neutral mutations and (4) contributes to program growth by making use of the genetic material available as structural introns. This comes along with a significant increase of LGP performance, which makes structural introns important for LGP.

Population dynamics linked to glacial cycles in Cercis chuniana F. P. Metcalf (Fabaceae) endemic to the montane regions of subtropical China.

The mountains of subtropical China are an excellent system for investigating the processes driving the geographical distribution of biodiversity and radiation of plant populations in response to Pleistocene climate fluctuations. How the major mountain ranges in subtropical China have affected the evolution of plant species in the subtropical evergreen broadleaved forest is an issue with long‐term concern. Here, we focused on Cercis chuniana, a woody species endemic to the southern mountain ranges in subtropical China, to elucidate its population dynamics. We used genotyping by sequencing (GBS) to investigate the spatial pattern of genetic variation among 11 populations. Geographical isolation was detected between the populations located in adjacent mountain ranges, thought to function as geographical barriers due to their complex physiography. Bayesian time estimation revealed that population divergence occurred in the middle Pleistocene, when populations in the Nanling Mts. separated from those to the east. The orientation and physiography of the mountain ranges of subtropical China appear to have contributed to the geographical pattern of genetic variation between the eastern and western populations of C. chuniana. Complex physiography plus long‐term stable ecological conditions across glacial cycles facilitated the demographic expansion in the Nanling Mts., from which contemporary migration began. The Nanling Mts. are thus considered as a suitable area for preserving population diversity and large population sizes of C. chuniana compared with other regions. As inferred by ecological niche modeling and coalescent simulations, secondary contact occurred during the warm Lushan–Tali Interglacial period, with intensified East Asia summer monsoon and continuous habitat available for occupation. Our data support the strong influence of both climatic history and topographic characteristics on the high regional phytodiversity of the subtropical evergreen broadleaved forest in subtropical China.

Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms.

MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems

This paper proposes a new Multi-Objective Plasma Generation Optimization (MOPGO) algorithm, and its non-dominated sorting mechanism is investigated for numerous challenging real-world structural optimization design problems. The Plasma Generation Optimization (PGO) algorithm is a recently reported physics-based algorithm inspired by the generation process of plasma in which electron movement and its energy level are based on excitation modes, de-excitation, and ionization processes. As the search progresses, a better balance between exploration and exploitation has a more significant impact on the results; thus, the crowding distance feature is incorporated in the proposed MOPGO algorithm. Also, the proposed posteriori method exercises a non-dominated sorting strategy to preserve population diversity, which is a crucial problem in multi-objective meta-heuristic algorithms. In truss design problems, minimization of the truss's mass and maximization of nodal displacement are considered objective functions. In contrast, elemental stress and discrete cross-sectional areas are assumed to be behavior and side constraints, respectively. The usefulness of MOPGO to solve complex problems is validated by eight truss-bar design problems. The efficacy of MOPGO is evaluated based on ten performance metrics. The results demonstrate that the proposed MOPGO algorithm achieves the optimal solution with less computational complexity and has a better convergence, coverage, diversity, and spread. The Pareto fronts of MOPGO are compared and contrasted with multi-objective passing vehicle search algorithm, multi-objective slime mould algorithm, multi-objective symbiotic organisms search algorithm, and multi-objective ant lion optimization algorithm. This study will be further supported with external guidance at https://premkumarmanoharan.wixsite.com/mysite.

Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression

Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming (GP). Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in GP. This work proposes a new novelty metric for entropy-based diversity measure for GP. The new novelty metric is based on the transformed semantics of models in GP, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help GP achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalization performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalize better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of GP.

A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.

An Enhanced Genetic Algorithm for Parameter Estimation of Sinusoidal Signals

Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.

A Hybrid Mechanism of Particle Swarm Optimization and Differential Evolution Algorithms based on Spark

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD’s partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks

Evolutionary Algorithms (EAs) are frequently used to solve various practical problems. It is common to adjust an EA to the solved problem by adding a different kind of problem-dependent mechanisms. One of the possible improvements is an evolutionary method hybridization with local search algorithms. Such hybridization may lead to phenomena called the Baldwin effect. It was shown that the occurrence of Baldwin effect helps to preserve population diversity and therefore may be beneficial for the method effectiveness. However, it also has its drawbacks. The use of local search causes the significant increase of computation load necessary for a single individual’s fitness computation. Therefore, the hybridization of an evolutionary method does not have to be beneficial. Moreover, the benefits (or drawbacks) of hybridization may be different depending on the method type and the features of the solved problem. Therefore, the main objective of this paper is to investigate the pros and cons of hybridization on the base of a hard practical and up-to-date problem, namely the Routing and Spectrum Allocation of Multicast Flows (RSA/M) in Elastic Optical Networks (EONs). The second objective of this paper is to propose an effective optimization method for solving the RSA/M problem in EONs.

Biogeography-based optimization with improved migration operator and self-adaptive clear duplicate operator

Biogeography-based optimization (BBO) is a new emerging population-based algorithm that has been shown to be competitive with other evolutionary algorithms. However, there are some insufficiencies in solving complex problems, such as poor population diversity and slow convergence speed in the later stage. To overcome these shortcomings, we propose an improved BBO (IBBO) algorithm integrating a new improved migration operator, Gaussian mutation operator, and self-adaptive clear duplicate operator. The improved migration operator simultaneously adopts more information from other habitats, maintains population diversity, and preserves exploitation ability. The self-adaptive clear duplicate operator can clear duplicate or almost identical habitats, while also preserving population diversity through a self-adaptation threshold within the evolution process. Simulation results and comparisons from the experimental tests conducted on 23 benchmark functions show that IBBO achieves excellent performance in solving complex problems compared with other variants of the BBO algorithm and other evolutionary algorithms. The performance of the improved migration operator is also discussed.

A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.