Niching Strategy Research Articles

Multi-objective niching quantum genetic algorithm-based optimization method for pneumatic hammer structure

Pneumatic down-the-hole (DTH) hammer drilling technology has been used extensively in various drilling applications owing to its high drilling efficiency, low gas consumption, non-polluting nature, and safety. Optimizing the hammer structure using empirical approaches is challenging due to the highly complex nonlinear problem caused by the piston movement. To address it, the intelligent algorithm is introduced for the first time and a multi-objective niching quantum genetic algorithm-based (MNQGA) optimization method for pneumatic hammer structure design is proposed in this study, to simultaneously maximize impact energy and minimize air consumption or optimize these objectives individually. Additionally, a novel niching strategy, corresponding adaptive quantum rotation gate, and mutation strategy are designed for the proposed method. The radial structures of a GQ127-type pneumatic hammer are optimized to maximize impact energy, followed by the optimization of the axial structures to satisfy four working conditions. The results show that different optimization objectives and constraints lead to different axial structural parameter values. After optimization, the hammer air consumption is reduced by at least 50.80 % at a constant impact energy of 350 J. The hammer impact energy is improved by more than 15.93 % if the volumetric flow rate of the supplied air is maintained at 10 m3/min. In addition, the effects of the hammer’s axial structure on its impact energy are investigated in detail based on an analytical model to validate the results of the proposed method. Five axial structure parameters are analyzed that have different effects on the impact energy. Within the given constraints, these parameters have their own optimal values that can be easily obtained using the proposed method. That is, the optimizations using the proposed method are consistent with those of the analytical models published in the literature. Therefore, the proposed method is efficient and accurate and can be used confidently when designing pneumatic hammers.

A coarse- and fine-grained niching-based differential evolution for multimodal optimization problems and its application in multirobot task allocation

Multimodal optimization problems (MOPs) need to locate the global optima as many as possible and refine the accuracy of the identified optima as high as possible. How to divide niches reasonably and search solutions effectively are two difficult problems facing MOPs. In this paper, a coarse- and fine-grained niching-based differential evolution (CFNDE) algorithm with micro-search strategy is proposed to divide population reasonably and search the optima effectively, which mainly includes four contributions. Firstly, a framework of coarse- and fine-grained niching (CFN) strategy is proposed, where the coarse-grained clustering method provides the information of clusters to the fine-grained clustering method to accurately divide the population into niches. Secondly, a micro-search-based mutation (MSM) strategy is proposed to accelerate the convergence of population, which searches for the optimal solution in the range between the current individual and the nearest individual. Thirdly, a multi-level local search (MLLS) strategy that incorporates the cluster-level and population-level search mechanisms is proposed to refine the accuracy of solutions. Finally, a stagnation detection and re-search (SDR) strategy is proposed to re-search the local optima in evolution. We compare CFNDE with the-state-of-art multimodal optimization algorithms on the widely used benchmark CEC’2013. The experimental results show that CFNDE performs better than or similar to some recently proposed algorithms in 17 out of 20 tested functions. Besides, the proposed CFNDE is also applied to the multirobot task allocation problems and CFNDE provides multiple completely different optimal scheduling schemes to deal with emergencies.

Minimum spanning tree niching-based differential evolution with knowledge-driven update strategy for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to simultaneously locate as many global optima as possible with high accuracy. Recently, many niching strategies have been wildly used in evolutionary algorithms to solve MMOPs due to the advantage of maintaining the diversity of population. However, most multimodal algorithms are sensitive to the niching parameters and lack effective methods to update the “stagnant individuals”, including individuals that have trapped into local optima or converged to the same optima. In this paper, a minimum spanning tree niching-based differential evolution (TNDE) with knowledge-driven update (KDU) strategy is proposed to better solve the above challenges, where the “knowledge” includes historical evolutionary information, fitness distribution information, and individual distribution information. In TNDE, a minimum spanning tree niching (MSTN) strategy is proposed to adaptively divide the population, which can adjust the number of niches dynamically. Besides, the KDU strategy is proposed to utilize the knowledge to locate and update stagnant individuals. Lastly, an improved differential evolution with local stage-based mutation (LSM) and directional guidance selection (DGS) strategies is proposed to accelerate convergence and refine the accuracy of solutions, respectively. The comparison results with 16 state-of-the-art algorithms on CEC’2013 show that TNDE achieves significant advantages on high-dimensional problems or problems with many global optima.

A network community-based differential evolution for multimodal optimization problems

Multimodal optimization problems (MMOPs) aim to locate multiple global optima simultaneously, which requires a high diversity of solutions. As a diversity preservation strategy customized for evolutionary algorithms (EAs), niching has been extensively incorporated into many algorithms to deal with MMOPs. However, many existing niching techniques require pre-defined parameters to control the size or number of niches, making the algorithm sensitive to parameters. Taking individuals as nodes, the inverse distance of two individuals as edges, and the historical information of recent generations as node attributes, we can construct a population as a network, whose purpose is to divide niches through community detection without relying on sensitive parameters. Inspired by this idea, this paper proposes a network community-based differential evolution for multimodal optimization problems (NetCDEMMOPs). In NetCDEMMOPs, three strategies are proposed and work together. Firstly, the community detection-based niching (CDN) strategy constructs the population as an attribute network and automatically divides it into multiple niches without sensitive parameters. Secondly, the community elites-based updating (CEU) strategy allocates more evolutionary resources to elite individuals of communities, then predicts and updates these elites based on historical information. Finally, the poor individual remolding (PIR) strategy remolds the last-ranking individuals and guides them toward promising positions. After extensive experimentation with the widely adopted CEC'2013 test suit, the experimental results reveal the superiority of NetCDEMMOPs over many successful or recent algorithms for MMOPs.

Integrating Sensor Ontologies with Niching Multi-Objective Particle Swarm Optimization Algorithm.

Sensor ontology provides a standardized semantic representation for information sharing between sensor devices. However, due to the varied descriptions of sensor devices at the semantic level by designers in different fields, data exchange between sensor devices is hindered. Sensor ontology matching achieves data integration and sharing between sensors by establishing semantic relationships between sensor devices. Therefore, a niching multi-objective particle swarm optimization algorithm (NMOPSO) is proposed to effectively solve the sensor ontology matching problem. As the sensor ontology meta-matching problem is essentially a multi-modal optimization problem (MMOP), a niching strategy is introduced into MOPSO to enable the algorithm to find more global optimal solutions that meet the needs of different decision makers. In addition, a diversity-enhancing strategy and an opposition-based learning (OBL) strategy are introduced into the evolution process of NMOPSO to improve the quality of sensor ontology matching and ensure the solutions converge to the real Pareto fronts (PFs). The experimental results demonstrate the effectiveness of NMOPSO in comparison to MOPSO-based matching techniques and participants of the Ontology Alignment Evaluation Initiative (OAEI).

MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles

Trajectory planning is a crucial task in designing the navigation systems of automatic underwater vehicles (AUVs). Due to the complexity of underwater environments, decision makers may hope to obtain multiple alternative trajectories in order to select the best. This paper focuses on the multiple-trajectory planning (MTP) problem, which is a new topic in this field. First, we establish a comprehensive MTP model for AUVs, by taking into account the complex underwater environments, the efficiency of each trajectory, and the diversity among different trajectories, simultaneously. Then, to solve the MTP, we develop an ant colony-based trajectory optimizer, which is characterized by a niching strategy, a decayed alarm pheromone measure, and a diversified heuristic measure. The niching strategy assists in identifying and maintaining a diverse set of high-quality solutions. The use of decayed alarm pheromone and diversified heuristic further improves the search effectiveness and efficiency of the algorithm. Experimental results on practical datasets show that our proposed algorithm not only provides multiple AUV trajectories for a flexible choice, but it also outperforms the state-of-the-art algorithms in terms of the single trajectory efficiency.

Finding robust influential seeds from networked systems against structural failures using a niching memetic algorithm

There has been a growing interest in analyzing the dynamics and characteristics of networked systems, and the influence maximization problem is a recent hotspot to determine influential seeds from nodal members. Widespread applicable values can be found in marketing and information digging tasks, which boost more attention on related studies. Several diffusion models and seed determination techniques have been developed. Meanwhile, networked systems are threatened by sabotages and structural perturbances, and recent literature indicated that the robustness of the diffusion process is significant in daily applications, which can be modeled as the robust influence maximization problem. However, existing studies have not touched upon the node-based attack and its impact on the performance of seeds; corresponding robust seeds determination strategies are also lacked. For solving the robust influence maximization problem considering node-based attacks, a numerical measure is designed in this paper to assess the performance of given seeds. A sensitivity analysis is also conducted to test the impairing effect of several nodal importance metrics. Further, guided by the proposed measure, a memetic algorithm with the niching strategy has been devised to search for seeds with robust influential ability, termed NMA-RIM. Experiments on synthetic and real-world networks validate the competitiveness of NMA-RIM over existing algorithms, and the improved efficiency achieved by the niching strategy is also demonstrated. Meanwhile, the difference between selection results guided by the proposed measure and previous ones is shown via empirical analysis. As demonstrated by the obtained results, NMA-RIM outperforms existing approaches, and a better computational efficiency can be achieved. Competitive candidates can be provided for decision makers to solve information diffusion dilemmas.

A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization

Whale optimization algorithm (WOA) has received increasing attention in engineering optimization owing to its high computation efficiency, whereas, it has exhibited the drawback of premature convergence in solving multi-dimensional engineering global optimization problems. In this research, a niching hybrid heuristic whale optimization algorithm (NHWOA) is proposed to enhance convergence speed and search coverage in solving global optimization problems. In the algorithm, the niching technique is introduced to promote the diversity of population and restrain premature convergence in search of a global best solution. A heuristic adjustment to the parameters of the hybrid WOA algorithm is made to promote the exploration potential of search agents in the evolution. A designed perturbation to all the search agents’ positions is executed to avoid their falling into a local optimum. Optimization to the CEC2014 benchmark functions as validation cases are conducted along with comparisons to both conventional intelligent optimization algorithms and other state-of-the-art modified WOAs. Results indicate the effectiveness and superiority of NHWOA in solving the problems. Five practical engineering problems for global optimization with multiply variables are introduced to validate the performance of the presented algorithm with good performance results in the global computations.

Matching Knowledge Graphs with Compact Niching Evolutionary Algorithm

To address the Knowledge Graph (KG) heterogeneity issue, we need to determine a set of entity correspondences, which requires aggregating several complementary similarity measures to improve the confidence of the results. How to determine the suitable aggregating weights for the similarity measures to improve the KG alignment’s quality is called the KG meta-matching problem, whose challenge of scalability remains significant in the Semantic Web (SW) domain. To face this challenge, this work proposes a Compact Niching Evolutionary Algorithm (CNEA) based matching technique. We first propose an approximate evaluation metric on the alignment’s quality, and on this basis, a multi-modal optimization model is constructed to formally define the KG meta-matching problem. Then, a niching strategy is combined with EA’s evolutionary paradigm to address it, which is able to effectively locate and maintain multiple global optimal solutions. Moreover, a serial matching framework and the compact evolutionary mechanism are presented to improve CNEA’s efficiency. In particular, the former reduces the algorithm’s search space of the instance matching phase with schema-level alignments, and the latter uses the probability representation on the population to reduce the algorithm’s memory consumption and run time. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)’s KG track to test our proposal’s performance, and experimental results show that CNEA-based KG matching technique is both effective and efficient.

Adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization

This paper proposes an adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization. In the proposed algorithm, a multi-niche sampling strategy is designed to sample a subpopulation for evolution at each generation. In this strategy, the entire population is firstly divided into multiple niches by employing a certain niching strategy at each generation. A subpopulation is then dynamically sampled from the resulting niches such that supporting a diverse search at the early stage of evolution while an intensive search towards the end of evolution. The above strategy will be further coupled with a neighborhood crossover, which is devised to encourage high potential solutions for exploitation while low potential solutions for exploration, thus appropriately searching the solution space. Additionally, an adaptive local search (ALS) scheme along with an adaptive elimination operation (AEO) have been designed. The ALS aims to appropriately fine-tune promising solutions in the sampled subpopulation while the AEO tends to adaptively eliminate unpromising individuals in the population during evolution. The performance of the proposed algorithm has been evaluated on CEC’2015 benchmark functions and compared with related methods. Experimental results show that our algorithm can achieve a superior performance and outperform related methods. The results also confirm the significance of devised strategies in the proposed algorithm.

Two-Stage Double Niched Evolution Strategy for Multimodal Multiobjective Optimization

In recent years, numerous efficient and effective multimodal multiobjective evolutionary algorithms (MMOEAs) have been developed to search for multiple equivalent sets of Pareto optimal solutions simultaneously. However, some of the MMOEAs prefer convergent individuals over diversified individuals to construct the mating pool, and the individuals with slightly better decision space distribution may be replaced by significantly better objective space distribution. Therefore, the diversity in the decision space may become deteriorated, in spite of the decision and objective diversities have been taken into account simultaneously in most MMOEAs. Because the Pareto optimal subsets may have various shapes and locations in the decision space, it is very difficult to drive the individuals converged to every Pareto subregion with a uniform density. Some of the Pareto subregions may be overly crowded, while others are rather sparsely distributed. Consequently, many existing MMOEAs obtain Pareto subregions with imbalanced density. In this article, we present a two-stage double niched evolution strategy, namely DN-MMOES, to search for the equivalent global Pareto optimal solutions which can address the above challenges effectively and efficiently. The proposed DN-MMOES solves the multimodal multiobjective optimization problem (MMOP) in two stages. The first stage adopts the niching strategy in the decision space, while the second stage adapts double niching strategy in both spaces. Moreover, an effective decision density self-adaptive strategy is designed for improving the imbalanced decision space density. The proposed algorithm is compared against eight state-of-the-art MMOEAs. The inverted generational distance union (IGDunion) performance indicator is proposed to fairly compare two competing MMOEAs as a whole. The experimental results show that DN-MMOES provides a better performance to search for the complete Pareto Subsets and Pareto Front on IDMP and CEC 2019 MMOPs test suite.

A niching cross-entropy method for multimodal satellite layout optimization design

Satellite layout optimization design (SLOD) relies on solving a high-dimensional and multimodal optimization problem, in which there exist multiple global optimal solutions. Existing algorithms for SLOD focus on seeking only one approximate global optimum. However, finding multiple solutions simultaneously could provide more design diversity for the designers. To alleviate this problem, multimodal optimization method is studied for SLOD in this paper, and an improved niching-based cross-entropy method (INCE) is proposed. INCE consists of an improved niching strategy, cross-entropy method-based offspring generation and a cross operator. CEC2013 benchmarks and satellite layout optimization design problem are investigated to verify the validity and feasibility of the proposed INCE. Compared with several state-of-the-art algorithms, the proposed algorithm performs better.

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.

Co-Evolutionary Niching Differential Evolution Algorithm for Global Optimization

Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, we present a co-evolutionary niching strategy (CoEN) to dynamically evolve appropriate niching methods and incorporate it into differential evolution (DE) to preserve the population diversity. The proposed CoEN strategy is achieved by optimizing a criterion, which involves both fitness improvement and population diversity resulting from employing the niching methods during evolution of DE. To verify the performance of proposed method, an extensive test on benchmark functions taken from CEC2019 and CEC2014 has been carried out. The results show the significance of proposed CoEN and, by incorporating CoEN, the resulting DE is able to achieve a better or competitive performance than related algorithms.

Multi-objective evolution strategy for multimodal multi-objective optimization

In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objective optimization problems. However, existing MOEAs cannot satisfactorily address multimodal multi-objective optimization problems that demand to find multiple groups of optimal solutions simultaneously. In this paper, we propose an evolution strategy to solve multimodal multi-objective optimization problems, named MMO-MOES. This paper focus on searching for well-converged and well-distributed solutions in the decision space. Firstly, a novel niching strategy in the decision space, which imitates the repulsive force among isotropic magnetic particles, is adopted to drive the individuals to preserve uniform distances from each other and spread to the whole Pareto set automatically. This strategy is effective in finding multiple groups of optimal solutions simultaneously. Secondly, MMO-MOES requires only a very small population size to obtain a well-distributed and well-converged set of Pareto optimal solutions in the decision space. The greater the population size, the clearer contour of the approximate Pareto sets and Pareto front will be. Finally, the MMO-MOES is compared against some chosen leading-edge MMOEAs. The experimental results demonstrate that MMO-MOES provides exceptional performance in searching for the complete Pareto subsets and Pareto front on Omni-test problem, Symmetrical Parts (SYM-PART) problems, and CEC 2019 Multimodal Multi-Objective Optimization Problems (MMOPs) test suite.

An aggregated pairwise comparison-based evolutionary algorithm for multi-objective and many-objective optimization

As the number of objectives increases, the ability of Pareto optimality in providing enough comparability among alternative solutions would be deteriorated seriously. In order to address this issue, this paper proposes a simple yet efficient fitness evaluation approach based on the piecewise aggregated pairwise comparisons (PAPC)11The aggregation of pairwise comparisons refers to that, for a solution x, its fitness value is the aggregation of the comparison results of x and other solutions in the population (Li et al., 2015). with the assistance of the two-stage selection strategy. The advantages of the proposed PAPC are threefold: (1) all the optimal solutions of the PAPC fitness being less than 0 are non-dominated solutions, (2) for the dominated solutions, PAPC is a metric of the distance to the non-dominated solution front, and (3) for the non-dominated solutions, PAPC rewards the diversity and penalizes the clustering behavior. Accordingly, PAPC can increase the comparability among candidate solutions with an explicit consideration of the convergence and diversity. Then, we develop a two-stage selection strategy and a niching strategy to assist PAPC to maintain diversity of solutions. We conduct experiments on a suite of test problems with up to 10 objectives, where the algorithm is also extended to handle constrained problems. The experimental results validate the effectiveness of the proposed algorithm on both multi-objective optimization problems and many-objective optimization problems.

An Edge IDS Based on Biological Immune Principles for Dynamic Threat Detection

Edge computing solves such questions as the massive multisource data and resource consuming computing tasks in edge devices. Some new security problems especially the data security and privacy issues have been introduced into the edge computing scenario. Through analyzing the biological immune principles, a novel idea for the problem of intrusion detection in edge computing is provided. Specifically, an edge intrusion detection system (Edge IDS) with a distributed structure, which has the characteristics of an imprecise model, self-learning, and strong interactivity, is constructed in a systematic way inspired by the biological immune principles. Moreover, a newly proposed gene immune detection algorithm (GIDA) is designed. In order that Edge IDS can deal with the dynamic data problem efficiently, the key functional components such as the remaining gene, niching strategy, and extracting vaccine are embedded into the GIDA algorithm. Furthermore, extensive simulation experiments are conducted, and the results show that the proposed Edge IDS can be adapted to the domain of edge computing with comparative performance advantages.

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.

Unimodal optimization using a genetic-programming-based method with periodic boundary conditions

This article describes a new genetic-programming-based optimization method using a multi-gene approach along with a niching strategy and periodic domain constraints. The method is referred to as Niching MG-PMA, where MG refers to multi-gene and PMA to parameter mapping approach. Although it was designed to be a multimodal optimization method, recent tests have revealed its suitability for unimodal optimization. The definition of Niching MG-PMA is provided in a detailed fashion, along with an in-depth explanation of two novelties in our implementation: the feedback of initial parameters and the domain constraints using periodic boundary conditions. These ideas can be potentially useful for other optimization techniques. The method is tested on the basis of the CEC’2015 benchmark functions. Statistical analysis shows that Niching MG-PMA performs similarly to the winners of the competition even without any parametrization towards the benchmark, indicating that the method is robust and applicable to a wide range of problems.

Automatic Planning of Multiple Itineraries: A Niching Genetic Evolution Approach

Automatic itinerary planning is a crucial and challenging issue in tourism. This paper proposes a novel automatic planning method to suggest multiple itineraries that satisfy the specific demands of tourists. First, a multiple-itinerary planning model is developed, which provides three customized goals for a tourist to choose and supports generating multiple $D$ -day trips. The model makes fewer assumptions than the literature works did, while it provides more flexibility to the tourists. Then, based on the multiple-itinerary planning model, we design a niching genetic evolution approach to accomplish the automatic itinerary planning task. The genetic evolution approach guarantees a high search efficiency, while the niching strategy facilitates maintaining the population diversity. Consequently, the resultant algorithm can finally provide a number of diverse and superior solutions. Experimental results on real-world datasets show that our proposed algorithm not only outperforms state-of-the-art methods in considering different user-specified goals, but it is also capable of generating a set of diverse itineraries for the tourist to select. Additional experiments further verify the scalability of the proposed algorithm in terms of the problem size and the optimization objective.