Deterministic Crowding Research Articles

A scalable, black-box hybrid genetic algorithm for continuous multimodal optimization in moderate dimensions

Optimization problems can be found in many areas of scienceand technology. Not only the global optimum, but also a(large) number of near-optima are often of interest. Thisgives rise to what are referred to as multimodal optimizationproblems. In most cases, the number and quality of the optimaare unknown and assumptions cannot be made about theobjective functions. In this paper, we focus on continuous,unconstrained optimization in moderately high-dimensionalcontinuous spaces (d ≤ 10).We present a scalablealgorithm with virtually no parameters, which performs wellfor general objective functions (non-convex, discontinuous).It is based on two well-established algorithms (CMA-ES,deterministic crowding). Novel elements of the algorithminclude the detection of seed points for local searches andcollision avoidance, both based on nearest neighbors, and astrategy for semi-sequential optimization to realize scalability.The performance of the proposed algorithm is numericallyevaluated using the CEC2013 niching benchmark suite for1 − 20 dimensional functions, and a 9 dimensional real-worldproblem from constraint optimization in climate research.The algorithm performs well in relation to the CEC2013benchmarks and only falls short on higher dimensional andstrongly inisotropic problems. In the case of the climate-related problem, the algorithm is able to find a high number(> 150) of optima of relevance to climate research. Theproposed algorithm does not require special configuration forthe optimization problems considered in this paper, i.e., itshows good black-box behavior.

Phase reassignment for load balance in low-voltage distribution networks

Load balancing is one of the most widely used techniques to reduce losses and improve service quality in low-voltage networks. Many methods have been proposed, including the customer phase reassignment, which has multiple advantages. However, its application represents a real challenge if the data availability is limited (only a few variables from some nodes are measured). This paper presents a new method for load balancing on a three-phase feeder by switching the phase connection of single-phase customers. It is based on Deterministic Crowding, a variant of Genetic Algorithms with few and easily adjustable parameters. In contrast to related works, we establish an imbalance target value and minimize the number of customers required to switch. The proposed method requires the phase current profiles from only a percentage of the customers, reducing the number of smart meters necessary and the related investment. The method was validated using real data, analyzing different scenarios with different percentages of measured customers compared on the basis of imbalance index, neutral current, and transformer lifespan reduction. Results show a significant improvement in load balancing with few changes, even when only 30% of customers are measured.

A niching differential evolution algorithm for the large-scale combined heat and power economic dispatch problem

The combined heat and power economic dispatch (CHPED) problem, which aims at minimizing system cost to supply system demand under various constraints, is one of the most challenging problems in the power system. Numerous researches are concentrated on solving the CHPED problem with small size, ignoring implementation of the large-scale CHPED problem with lots of local optimal solutions. This paper proposes a niching differential evolution algorithm (NDE), which incorporates niching methods into differential evolution algorithm (DE), to solve the large-scale CHPED problem. In NDE, a modified DE/best/1 strategy inspired by the neighborhood concept of niching methods, called DE/best/1/nh, is developed to generate diverse solutions. A two-phase selection that utilizes the deterministic crowding of niching methods and greedy selection is designed to remain promising solutions. The two strategies help to balance the global and local search capabilities of the algorithm. The performance of NDE is evaluated by a comparative analysis with other existing approaches on the five CHPED systems with 7, 24, 84, 96 and 192 units. The numerical results demonstrate the feasibility and effectiveness of NDE in solving the large-scale CHPED problem.

A new data-driven method based on Niching Genetic Algorithms for phase and substation identification

Knowledge about the customers’ phase connections is strategic and critical for utility companies. It allows them to optimize maintenance and repair operations, implement load balancing, and detect losses, among other benefits. However, this information may be incomplete or outdated due to the undocumented changes in the Low Voltage network. Several methods have been proposed to estimate it. Methods based on data analysis stand out because they do not require costly specialized equipment. This work presents a new method for Phase Identification and Transformer Substation Detection for single-phase customers. Unlike previous approaches, we address the problem through a heuristic optimization, using an Evolutionary Algorithm based on Deterministic Crowding and correlation analysis on load measurements. The algorithm was designed to work with low penetration of smart meters and missing data, obtaining better results in shorter periods. The method was tested using both a public dataset and a dataset from Tucumán province, Argentina. We obtained an average accuracy above 95% on 21 days if almost 30% of the smart meters are available (200 customers in total). In contrast, only 5 days are required to reach the same accuracy if more than 80% of smart meters are available.

A vision system for canning with fish sensing using rule‐based matching and segmentation

AbstractWe develop a machine vision system as a key component of a robot‐assisted packaging system, which can guide the robot arms to pack the roast sauries into cans. For gripping strategy generation, the system is required not only to be able to detect the roast saury area but also estimate the geometric parameters. Besides, according to different canning requirements, it is also necessary to distinguish the type of fish parts. Facing these challenges, we propose a novel rule‐based matching method combined with an improved efficient graph‐based image segmentation (EGIS) method for sensing the fish part. Specifically, the matching method applies our originally designed rule‐based similarity under a genetic algorithm framework combined with deterministic crowding technique, which is used to sensing one type of fish parts. On the other hand, we improve the EGIS by introducing a shape restriction to deal with leftover fish parts. The experiments are implemented for two different types of fish part in the real factory environment. The result of our method achieved a mean location accuracy of 93.5% with a practical average processing time of 2.6 s per image. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Deterministic crowding introducing the distribution of population for template matching

This paper explains deterministic crowding (DC), introducing the distribution of population for template matching. We apply a simple genetic algorithm (GA) to template matching because this approach is effectively able to optimize geometric transformation parameters, such as parallel transformation, scaling, and in‐plane rotation. However, since the simple GA can obtain only one global optimum, detecting multiple objects is difficult. This is not of practical use. In order to detect multiple objects, we focus on DC, which is a multimodal optimization method and able to obtain multiple global and local solutions. In DC, there is a drawback where many individuals converge to one object and, hence, some objects cannot be detected. In order to solve this problem, the proposed method introduces the distribution of population. In experiments, the proposed method, DC, and crowding are applied to template matching and compared. The results confirm that the proposed method is better when an optimal threshold, which is used to create a cluster, is set. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A niching chaos optimization algorithm for multimodal optimization

Niching is the technique of finding and preserving multiple stable niches, or favorable parts of the solution space possibly around multiple optima, for the purpose of solving multimodal optimization problems. Chaos optimization algorithm (COA) is one of the global optimization techniques, but as far as we know, a niching variant of COA has not been developed . In this paper, a novel niching chaos optimization algorithm (NCOA) is proposed. The circle map with a proper parameter setting is employed considering the fact that the performance of COA is affected by the chaotic map. In order to achieve niching, NCOA utilizes several techniques including simultaneously contracted multiple search scopes, deterministic crowding and clearing. The effects of some components and parameters of NCOA are investigated through numerical experiments. Comparison with other state-of-the-art multimodal optimization algorithms demonstrates the competitiveness of the proposed NCOA.

A Survey on Particle Swarm Optimization Algorithms for Multimodal Function Optimization

Many scientific and engineering applications involve finding more than one optimum. A comprehensive review of the existing works done in the field of multimodal function optimization was given and a critical analysis of the existing methods was also provided. Several techniques in solving multimodal function optimization problems were introduced, such as clearing, deterministic crowding, sharing, species conserving and so on. And we summarized defects of existing algorithms: lacking of self-adaptive adjustment function, requiring setting some parameters according to different problems, lacking of unified theoretical and experimental system to guide algorithms design and not maintaining the diversity of swarm. Moreover, most of existing multimodal particle swarm optimization algorithms which include SPSO, MSPSO, ESPSO, ANPSO, kPSO, MGPSO, AT-MGPSO, rpso, and SDD-PSO were described and compared and advantages and disadvantages existing in these algorithms were pointed out. Therefore, some ideas to improve the performance of multimodal function optimization algorithms were proposed.

Analysis of Diversity-Preserving Mechanisms for Global Exploration

Maintaining diversity is important for the performance of evolutionary algorithms. Diversity-preserving mechanisms can enhance global exploration of the search space and enable crossover to find dissimilar individuals for recombination. We focus on the global exploration capabilities of mutation-based algorithms. Using a simple bimodal test function and rigorous runtime analyses, we compare well-known diversity-preserving mechanisms like deterministic crowding, fitness sharing, and others with a plain algorithm without diversification. We show that diversification is necessary for global exploration, but not all mechanisms succeed in finding both optima efficiently. Our theoretical results are accompanied by additional experiments for different population sizes.

The Crowding Approach to Niching in Genetic Algorithms

A wide range of niching techniques have been investigated in evolutionary and genetic algorithms. In this article, we focus on niching using crowding techniques in the context of what we call local tournament algorithms. In addition to deterministic and probabilistic crowding, the family of local tournament algorithms includes the Metropolis algorithm, simulated annealing, restricted tournament selection, and parallel recombinative simulated annealing. We describe an algorithmic and analytical framework which is applicable to a wide range of crowding algorithms. As an example of utilizing this framework, we present and analyze the probabilistic crowding niching algorithm. Like the closely related deterministic crowding approach, probabilistic crowding is fast, simple, and requires no parameters beyond those of classical genetic algorithms. In probabilistic crowding, subpopulations are maintained reliably, and we show that it is possible to analyze and predict how this maintenance takes place. We also provide novel results for deterministic crowding, show how different crowding replacement rules can be combined in portfolios, and discuss population sizing. Our analysis is backed up by experiments that further increase the understanding of probabilistic crowding.

Crowding clustering genetic algorithm for multimodal function optimization

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches and clustering operation to eliminate genetic drift. Numerical experiments on standard test functions indicate that crowding clustering genetic algorithm is superior to both standard crowding and deterministic crowding in quantity, quality and precision of multi-optimum search. The proposed algorithm is applied to the practical optimal design of varied-line-spacing holographic grating and achieves satisfactory results.

Evolutionary Search for Deep-Space Science Mission Orbits

We present an application of an evolutionary programming method, niching genetic algorithms, to the search for orbits in the spatial elliptic restricted three-body problem (ER3BP) that is suitable for deep-space science missions. The niching method used is deterministic crowding, which renders a global optimization while permitting for several optimal and suboptimal solutions to coexist. This novel approach yields diverse probing of the state space of the ER3BP. From the practical standpoint, the orbits found remain within a bounded distance from Earth, thus allowing high data-rate communication while ensuring safe operational environment, far from thermal perturbations and visual occultation as well as Earth’s magnetic and radiation fields.

Niching method using clustering crowding

This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that, the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.

The niching method for obtaining global optima and local optima in multimodal functions

AbstractSometimes it is desirable to know the secondary candidates as well as the global optima of multimodal functions. The Deterministic Crowding (DC) method is an effective type of genetic algorithm for discovering multiple global optima, but it has difficulties discovering local optima. An improvement on this method called “Dispersing Deterministic Crowding” (DDC) is therefore proposed which encourages dispersion of individuals and creation of species within the population in order to increase the discovery of local optima, as well as of global optima. A method for preferential identification of solutions with a fitness exceeding some demanded level is also developed. The performance of DDC is compared with those of other niching methods, DC, sharing, RTS, GA with tabu search, and the immune algorithm, to show its effectiveness. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(11): 30–42, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10480

Niching genetic algorithms-based characterization of geocentric orbits in the 3D elliptic restricted three-body problem

This paper investigates the application of niching genetic algorithms to the characterization of families of geocentric orbits in the Sun–Earth spatial elliptic restricted three-body problem (ER3BP). The niching method used is deterministic crowding, which renders global optimization while permitting for several optimal and sub-optimal solutions to co-exist. This novel approach yields diverse probing of the state-space of the ER3BP. From the practical standpoint, the orbits found remain within a bounded distance from Earth, thus allowing high data-rate communication while ensuring safe operational environment far from thermal perturbations and visual occultations as well as Earth’s magnetic and radiation fields.

Characterization and design of out-of-ecliptic trajectories using deterministic crowding genetic algorithms

This paper investigates the application of Deterministic Crowding Genetic Algorithms to the characterization and design of special trajectories in the spatial circular restricted three-body problem with the Sun and the Earth as the primary gravitational bodies. These trajectories are characterized by large displacements normal to the Sun–Earth ecliptic plane. This attribute renders them particularly suitable for space-borne infrared observatories, because the normal component of motion results in a significantly reduced noise from the interplanetary (zodiacal) dust and a concomitant reduction in the necessary size of the optical collecting area. The characterization process yields three new types of trajectories with large normal displacement. By using the results of the characterization process, we continue with genetic algorithms-based trajectory design, which yields promising results in terms of reduction of the zodiacal dust noise.