Niching Algorithms Research Articles

Niching Global Optimisation: Systematic Literature Review

Niching in global optimisation refers to a set of techniques designed to identify multiple optimal solutions within a nonlinear, multimodal landscape. These algorithms enhance the exploratory capabilities of conventional metaheuristics by maintaining diversity and supporting coexisting subpopulations across a search space, thereby allowing a more deterministic approach to the true global optimum. Niching algorithms can be categorised into three primary subfamilies: sequential or temporal niching, parallel or spatial niching, and hybrid models which integrate various niching subparadigms. This research paper aims to explore the effectiveness and limitations of different niching algorithms by providing a systematic literature review of the theoretical frameworks within these subfamilies. Eleven major niching native subparadigms have been identified: fitness sharing, crowding, clearing, speciation, restricted tournament selection, clustering, multiobjectivisation, embedded hybrid methods, ensemble hybrid methods, and other hybrid approaches. This study offers a detailed examination of each paradigm’s theoretical foundation, including template algorithmic layouts, and delineates the unique elements of each approach. Research contributions from the inception of niching to 2024 have been aggregated from the SCOPUS database and systematically classified. Data aggregation included journal articles, conference papers, review papers, and research reports published in English only following the PRISMA framework. Application papers with novel theoretical ideas were also taken into account. In all, 203 research works were retained under the inclusion and exclusion criteria. This study concludes with overarching high-level recommendations for future research in modern niching optimisation, emphasising the development of space and time-scalable methods to enhance the adaptability and efficiency of optimisation algorithms in diverse, increasingly multivariable multimodal problems.

Cutting-edge research in physics engines: Exploring parallel technologies, algorithm insights, and upcoming trends

Physics engines, a quintessential product of computer science, have become indispensable tools in simulating physical phenomena through mathematical computations. Their prominence in game development, engineering simulations, and various physics applications is unquestionable. Ensuring their optimization is pivotal for meeting the rigorous demands of practical experiments. The significance of this study is twofold: theoretical foundations and practical necessities. From a theoretical standpoint, this paper delves into the core principles and architectural underpinnings of physics engines. We venture into the integration of multithreading and the application of niche algorithms within these engines. Further analysis illuminates how leveraging these technologies can drastically enhance the performance of a physics engine. In culmination, we propose a robust, feasible theoretical framework for physics engines, filling in the pivotal details. This exploration aims to galvanize and steer the evolution of physics engine design. Addressing practical necessities, our research emphasizes real-world applications of various physics engines. We examine tailored optimization strategies suited for specific needs and investigate methodologies to elevate the operational efficiency of specialized physics engines. This dimension of our study holds substantial real-world implications.

Topology Optimization Combined with a Parametric Algorithm for Industrial Synchronous Reluctance Motor Design

In this paper, we propose combining topology optimization with a parametric algorithm (TOCPA) to design an industrial synchronous reluctance motor (SynRM) that satisfies super-premium efficiency (IE4). TOCPA consists of two main processes. An improved niching algorithm is proposed as an existing parametric algorithm, and multiple optimal points were identified quickly and effectively within the design range specified by the user. Existing parametric algorithms lack the ability to find unpredictable new optimal shapes; therefore, topology optimization has been added. TOCPA converges the two optimization methods to compensate for the shortcomings of each and maximizes the advantages. In addition, the boundary surface ON–OFF method is applied to accelerate the topology optimization process. Subsequent ratio-based smoothing techniques smooth out discontinuous interfaces and remove clusters to improve the torque characteristics and reduce the manufacturing difficulty. Finally, the torque ripple was reduced using the skew technique, and mechanical stability was confirmed using a stress analysis. An IE4 industrial SynRM was successfully designed using TOCPA.

EXPERIMENTAL ANALYSIS OF MULTINATIONAL GENETIC ALGORITHM AND ITS MODIFICATIONS

Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.

Optimized Design of Permanent Magnet Assisted Synchronous Reluctance Motor Using Oriented Auto-tuning Niching Algorithm

In this paper, an oriented auto-tuning niching algorithm (OANA) is proposed for the design optimization of a permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for a pedal-assist system electric bicycle (PAS-EB). The OANA is a fast and accurate optimization algorithm for finding the optimal points in multi-modal functions. Specifically, the OANA remedies the shortcomings of the conventional auto-tuning niching genetic algorithm by adopting a probability method that is based on the navigational pathways. The OANA showed excellent performance by utilizing mathematical multi-modal test functions. Then, the OANA was applied to cogging torque optimization for a PMa-SynRM design of the PAS-EB and found several optimal points. Finally, the optimum design is derived by comparing the average torque and torque ripple of the obtained design points.

Niching comprehensive learning gravitational search algorithm for multimodal optimization problems

Many niching algorithms have been proposed in recent times to cater complex real-world problems having several global/local optima. These algorithms help in locating as many global/local optima as possible. However, to explore the search space along with locating and maintaining a large number of global solutions is always a challenging problem. Hence, we propose a new niching strategy named, ‘niching Comprehensive Learning Gravitational Search algorithm’, in which the CLGSA algorithm is used to solve complex problems having multiple solutions. The proposed algorithm first efficiently tries to explore the search space without trapping in local optima’s and secondly it locates all possible global optima’s. This algorithm has an external memory that enhances its capability to find the maximum number of optima’s. The proposed approach is tested on the IEEE CEC-2013 Multimodal Optimization benchmark problems (Li et al. in Evolutionary computation and machine learning group, RMIT University, Melbourne, Australia, Technical Report, 2013). The results of the proposed scheme are compared with five other state-of-the-art methods. In order to show the efficiency of CLGSA algorithm over real-life multimodal optimization problems, it applied to non-linear, and a mixed variable problem this is, the Reactive Power Dispatch problem (RPD). A standard benchmark IEEE-57 bus system is considered to evaluate the efficiency of the CLGSA. The evaluated simulation results show that CLGSA algorithm successfully solve multimodal problems and the RPD problem with significant accuracy.

Diversity Measures for Niching Algorithms

Multimodal problems are single objective optimisation problems with multiple local and global optima. The objective of multimodal optimisation is to locate all or most of the optima. Niching algorithms are the techniques utilised to locate these optima. A critical factor in determining the success of niching algorithms is how well the search space is covered by the candidate solutions. For niching algorithms, high diversity during the exploration phase will facilitate location and identification of many solutions while a low diversity means that the candidate solutions are clustered at optima. This paper provides a review of measures used to quantify diversity, and how they can be utilised to quantify the dispersion of both the candidate solutions and the solutions of niching algorithms (i.e., found optima). The investigated diversity measures are then used to evaluate the distribution of candidate solutions and solutions when the enhanced species-based particle swarm optimisation (ESPSO) algorithm is utilised to optimise a selected set of multimodal problems.

Crack Detection in Pavement Images Based on a Self- Adaptive Niche Algorithm

ABSTRACT This study focuses on optical image pavement damage detection instead of artificial detection in pavement maintenance. Based on the characteristics of cracks and combined with the niche theory, it proposes a dynamic adaptive curve extraction algorithm. First, we construct the matrix space, map the original pavement image data to the target space, process the data in target space using the multi-trough algorithm, then connect the extreme gray value points between two adjacent scanning rows with lines, compare the average gray value of the line with the average gray value of this area, and judge the possibility of cracks according to curve extension characteristics. The method considers oil, water stain, irregular concave spot, and other kinds of image noise on the pavement surface. It has good adaptability, and experimental results show that the algorithm is effective.

Double-layer-clustering differential evolution multimodal optimization by speciation and self-adaptive strategies

Multimodal optimization aims to find and maintain as many global and local optima of a function as possible. Niching techniques based on multi-populations and clustering proved to be efficient for tackling multimodal optimization problems. The main focus of this work is to enhance the diversity of the population and improve the global search ability to locate more optima. A Double-Layer-Clustering Speciation Differential Evolution (DLCSDE) algorithm for multimodal optimization is proposed. We also show how the DLCSDE can be improved by integrating with a self-adaptive strategy to form the Self-adaptive DLCSDE (SDLCSDE). Based on speciation, first layer clustering divides the entire population into multiple subpopulations to locate global and local optima. The seeds from each species then form a sub-population to search globally during the second layer clustering to find peaks missed during the first layer clustering search process. To test the performance, both DLCSDE and SDLCSDE are compared with 17 state-of-art niching algorithms on 29 multimodal problems with different dimensions. The experiment results demonstrate that both the proposed algorithms outperform or perform comparably to the 17 niching algorithms on all the test functions.

Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization

In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing performance. Hence, this paper has two main contributions. First, two novel parameter-free neighborhood structures named Topological Nearest-Better (TNB) neighborhood and Distance-based Nearest-Better (DNB) neighborhood are proposed in the topological space and decision space, respectively. Second, two proposed neighborhoods are combined with Fuzzy PSO (FPSO) and two novel niching algorithms, called TNB-FPSO and DNB-FPSO, are proposed for solving multimodal optimization problems. It should be noted that we use a zero-order fuzzy system to balance between exploration and exploitation in the proposed algorithms. To evaluate the performance of proposed algorithms, we performed a detailed empirical evaluation on the several standard multimodal benchmark functions. Our results show that DNB-FPSO statistically outperforms the other compared multimodal optimization algorithms.

A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning

Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.

POTENTIAL GEOGRAPHIC DISTRIBUTION OF THE GENUS Micrurus WAGLER, 1824 (SERPENTES: ELAPIDAE) AND ANTIVENOM SUPPLY IN RIO DE JANEIRO STATE, BRAZIL

The coral snake genus Micrurus has non-aggressive behavior, being responsible for only 0.4% of the reports of snakebites in Brazil. However, although rare, accidents are potentially serious due to rapidly evolving respiratory paralysis. In the last few years, there was an abrupt reduction in the number of hospitals receiving antivenom support in Rio de Janeiro State, and currently the Health Ministry of Brazil distributes the antivenom only to 25 hospitals in 21 municipalities, even so with no data on specific serum provided by each hospital. This study aimed to determine the potential distribution of Micrurus species in Rio de Janeiro State in order to compare potential areas for risk of elapidic accidents with the localities assisted by hospitals with antivenom distribution. We performed ecological niche models to generate maps of potential distribution for all species through several niche algorithms. Based on these results we analyzed and discussed the consequences of this change in the number of hospitals receiving antivenom. Our results revealed the potential occurrence of the genus throughout the State territory and show a partially homogeneous distribution of health units, although the northern region represents an area with a deficiency in hospital coverage. According to our results and data from the Rio de Janeiro State Secretary of Health, it is highly desirable that at least some antivenom service centers in the regions “Metropolitana” and “Baixadas Litorâneas” receive antielapidic serum and that there be an increase in the number of units in “Norte Fluminense” and “Noroeste Fluminense” regions of Rio de Janeiro State.

Climate change will decrease the range of a keystone fish species in La Plata River Basin, South America

Climate change threatens freshwater fish by severely modifying water quality and hydrological dynamics, hence altering the species distribution. We assessed the climate change effects on the geographical distribution of Salminus brasiliensis, a keystone species of economic interest in the La Plata River basin. Using ecological niche models, we estimated the species range in the present time and assessed the range shift phenomena through climatically suitable areas in the future. We also quantified the predictive uncertainty from niche models, atmosphere–ocean general circulation models, and carbon emission scenarios. Our predictions indicated a great range contraction of S. brasiliensis in the future. The south-central portion of the basin should retain the climate refuge function for the species at 2050. Nonetheless, the segregation of this climate refuge in two smaller parts was predicted at the end of the century. Our study also revealed that the greatest source of uncertainty in forecasts of species range shifts arises from using alternative niche algorithms in modeling process. Our results contribute to more effective measures for conservation of S. brasiliensis, thus helping to ensure the ecosystem processes and socioeconomic activities in the basin dependent on this species.

Constrained niching using differential evolution

Abstract The work presented here involves development and detailed investigations of niching methods for multimodal optimization of constrained functions. There is a lack of investigations in the literature on constrained multimodal optimization, hence a number of constrained niching algorithms have been developed here that leverage existing differential evolution-based niching methods with a feasibility rules-domination selection procedure. Furthermore, a suite of 18 benchmark functions has been developed and are presented in this paper; nine newly developed functions are incorporated with nine existing functions from the literature. Optimization results on these analytical functions using the constrained niching algorithms are presented, with analysis provided on the ability to locate multiple global optima, the convergence speed and constraint handling nature of the methods. The differential evolution strategy is also investigated, with SHADE and L-SHADE strategies considered. Finally, a dimensionality study also compares against the only other known constrained niching algorithm. Results indicate that all of the algorithms developed and tested generally perform well for low dimensional, low modality problems, but that local neighbourhood-based methods show the best results across the suite of functions tested. When high-dimensional problems are considered, using the L-SHADE strategy yields excellent results. An accompanying supplementary data file is provided with the manuscript.

Ensemble strategies for population-based optimization algorithms – A survey

In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, tournament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem. Recently, the concept of incorporating ensemble strategies into POAs has become popular so that the process of configuring an optimization algorithm can benefit from both the availability of diverse approaches at different stages and alleviate the computationally intensive offline tuning. In addition, algorithmic components of different advantages could support one another during the optimization process, such that the ensemble of them could potentially result in a versatile POA. This paper provides a survey on the use of ensemble strategies in POAs. In addition, we also provide an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. and compare them with the ensemble strategies in the context of POAs.

A novel optimal fuzzy integrated control method of active suspension system

A novel optimal fuzzy integrated control method is proposed and used to a half-car model with active suspension system. The cultural algorithm improved by the use of a dynamic acceptance function associated with niche algorithm is presented to optimize the fuzzy control rules with 4 input variables and 2 output variables. The vertical and rolling vibration accelerations of the bodywork are selected as an integrated control objective by using a fitness function. The simulation results of control system demonstrate that there are evident reductions in both vertical vibration acceleration and rolling angle acceleration if fuzzy control rules are optimized by the modified cultural algorithm and then applied to the active suspension system. It is simultaneously shown that the vertical displacement and rolling angle displacement also experience reduction processes. All of the numerical analyses give enormous supports to use of the proposed control scheme.

Learning Multimodal Parameters: A Bare-Bones Niching Differential Evolution Approach.

Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning. Targeting at locating multiple optima simultaneously in the multimodal landscape, this paper develops an efficient neighborhood-based niching algorithm. Bare-bones differential evolution is used as the baseline. Further, using Gaussian mutation with local mean and standard deviations, the neighborhoods capture niches that match well with the contours of peaks in the landscape. To increase diversity and enhance global exploration, the proposed algorithm embeds a diversity preserving operator to reinitialize converged or overlapped neighborhoods. The experimental results verify that the proposed algorithm has superior and consistent performance for a wide range of MMOP problems. Further, the algorithm has been successfully applied to train neural network ensembles, which validates its effectiveness and benefits of learning multimodal parameters.

Novel benchmark functions for continuous multimodal optimization with comparative results

Multi-modal optimization is concerned with locating multiple optima in one single run. Finding multiple solutions to a multi-modal optimization problem is especially useful in engineering, as the best solution may not always be the best realizable due to various practical constraints. To compare the performances of multi-modal optimization algorithms, multi-modal benchmark problems are always required. In this paper, 15 novel scalable multi-modal and real parameter benchmark problems are proposed. Among these 15 problems, 8 are extended simple functions while the rest are composition functions. These functions coordinate rotation and shift operations to create linkage among different dimensions and to place the optima at different locations, respectively. Four typical niching algorithms are used to solve the proposed problems. As shown by the experimental results, the proposed problems are challenging to these four recent algorithms.

An optimal vibration control strategy for a vehicle's active suspension based on improved cultural algorithm

This research is conducted such that the bodywork vertical vibration acceleration of a quarter car model is selected as a control objective and Fuzzy-PID control strategy based on improved cultural algorithm is proposed for vibration reduce. Cultural algorithm and niche algorithm are mixed together to design Fuzzy-PID controller and optimize control rules, which can accelerate optimization speed and has a good global optimal performance. The numerical results show that the active suspension, with Fuzzy-PID control strategy in which control rules are optimized by improved cultural algorithm, can significantly suppress the bodywork vertical vibration acceleration, and the ride comfort is improved. All the simulation analyses can give the support for the optimal control scheme.

An Improved Parent-Centric Mutation With Normalized Neighborhoods for Inducing Niching Behavior in Differential Evolution

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.