The Experts below are selected from a list of 53577 Experts worldwide ranked by ideXlab platform
Ronghua Shang - One of the best experts on this subject based on the ideXlab platform.
-
a novel selection Evolutionary Strategy for constrained optimization
Information Sciences, 2013Co-Authors: Licheng Jiao, Fang Liu, Ronghua Shang, Rustam StolkinAbstract:The existence of infeasible solutions makes it very difficult to handle constrained optimization problems (COPs) in a way that ensures efficient, optimal and constraint-satisfying convergence. Although further optimization from feasible solutions will typically lead in a direction that generates further feasible solutions, certain infeasible solutions can also provide useful information about the optimal direction of improvement for the objective function. How well an algorithm makes use of these two solutions determines its performance on COPs. This paper proposes a novel selection Evolutionary Strategy (NSES) for constrained optimization. A self-adaptive selection method is introduced to exploit both informative infeasible and feasible solutions from a perspective of combining feasibility with multi-objective problem (MOP) techniques. Since the global optimal solution of a COP is a feasible non-dominated solution, both non-dominated solutions with low constraint violation and feasible ones with low objective values are beneficial to an evolution process. Thus, the exploration and exploitation of both of these two kinds of solutions are preferred during the selection procedure. Several theorems and properties are given to prove the above assertion. Furthermore, the performance of our method is evaluated using 22 well-known benchmark functions. Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of the speed of finding feasible solutions and the stability of converging to global optimal solutions. In particular, when dealing with problems that have zero feasibility ratios and more than one active constraint, our method provides feasible solutions within fewer fitness evaluations (FES) and converges to the optimal solutions more reliably than other popular methods from the literature.
-
Immune clonal selection Evolutionary Strategy for constrained optimization
Lecture Notes in Computer Science, 2006Co-Authors: Licheng Jiao, Maoguo Gong, Ronghua ShangAbstract:Based on the clonal selection theory, a novel artificial immune systems algorithm, immune clonal selection Evolutionary Strategy for constrained optimization (ICSCES), is put forward. The new algorithm uses the stochastic ranking constraint-handling technique, realizs local search using clonal proliferation and clonal selection, and global search using clonal deletion. The experimental results on ten benchmark problems show, compared with the (μ,λ) Evolutionary strategies adopting stochastic ranking technique and dynamic penalty function method, ICSCES has the ability of significantly improving the search performance both in convergence speed and precision.
-
PRICAI - Immune clonal selection Evolutionary Strategy for constrained optimization
Lecture Notes in Computer Science, 2006Co-Authors: Licheng Jiao, Maoguo Gong, Ronghua ShangAbstract:Based on the clonal selection theory, a novel artificial immune systems algorithm, immune clonal selection Evolutionary Strategy for constrained optimization (ICSCES), is put forward. The new algorithm uses the stochastic ranking constraint-handling technique, realizs local search using clonal proliferation and clonal selection, and global search using clonal deletion. The experimental results on ten benchmark problems show, compared with the (µ, λ)Evolutionary strategies adopting stochastic ranking technique and dynamic penalty function method, ICSCES has the ability of significantly improving the search performance both in convergence speed and precision.
Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.
-
a novel selection Evolutionary Strategy for constrained optimization
Information Sciences, 2013Co-Authors: Licheng Jiao, Fang Liu, Ronghua Shang, Rustam StolkinAbstract:The existence of infeasible solutions makes it very difficult to handle constrained optimization problems (COPs) in a way that ensures efficient, optimal and constraint-satisfying convergence. Although further optimization from feasible solutions will typically lead in a direction that generates further feasible solutions, certain infeasible solutions can also provide useful information about the optimal direction of improvement for the objective function. How well an algorithm makes use of these two solutions determines its performance on COPs. This paper proposes a novel selection Evolutionary Strategy (NSES) for constrained optimization. A self-adaptive selection method is introduced to exploit both informative infeasible and feasible solutions from a perspective of combining feasibility with multi-objective problem (MOP) techniques. Since the global optimal solution of a COP is a feasible non-dominated solution, both non-dominated solutions with low constraint violation and feasible ones with low objective values are beneficial to an evolution process. Thus, the exploration and exploitation of both of these two kinds of solutions are preferred during the selection procedure. Several theorems and properties are given to prove the above assertion. Furthermore, the performance of our method is evaluated using 22 well-known benchmark functions. Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of the speed of finding feasible solutions and the stability of converging to global optimal solutions. In particular, when dealing with problems that have zero feasibility ratios and more than one active constraint, our method provides feasible solutions within fewer fitness evaluations (FES) and converges to the optimal solutions more reliably than other popular methods from the literature.
-
PRICAI - Immune clonal selection Evolutionary Strategy for constrained optimization
Lecture Notes in Computer Science, 2006Co-Authors: Licheng Jiao, Maoguo Gong, Ronghua ShangAbstract:Based on the clonal selection theory, a novel artificial immune systems algorithm, immune clonal selection Evolutionary Strategy for constrained optimization (ICSCES), is put forward. The new algorithm uses the stochastic ranking constraint-handling technique, realizs local search using clonal proliferation and clonal selection, and global search using clonal deletion. The experimental results on ten benchmark problems show, compared with the (µ, λ)Evolutionary strategies adopting stochastic ranking technique and dynamic penalty function method, ICSCES has the ability of significantly improving the search performance both in convergence speed and precision.
-
Immune clonal selection Evolutionary Strategy for constrained optimization
Lecture Notes in Computer Science, 2006Co-Authors: Licheng Jiao, Maoguo Gong, Ronghua ShangAbstract:Based on the clonal selection theory, a novel artificial immune systems algorithm, immune clonal selection Evolutionary Strategy for constrained optimization (ICSCES), is put forward. The new algorithm uses the stochastic ranking constraint-handling technique, realizs local search using clonal proliferation and clonal selection, and global search using clonal deletion. The experimental results on ten benchmark problems show, compared with the (μ,λ) Evolutionary strategies adopting stochastic ranking technique and dynamic penalty function method, ICSCES has the ability of significantly improving the search performance both in convergence speed and precision.
-
Immune-Evolutionary Strategy and its application
6th International Conference on Signal Processing 2002., 1Co-Authors: Jianguo Zheng, Fang Liu, Licheng JiaoAbstract:A novel global parallel algorithm, the immune-Evolutionary Strategy (IES), is proposed for rule extraction in data-mining; it combines the immune mechanism and the Evolutionary mechanism. The main idea of IES is to utilize theoretically background knowledge and local characteristic information for seeking ways and means of finding the optimal solution when dealing with difficult problems. IES is illustrated to be able to improve the stability of the population, increase the holistic performance and make the rules extracted have higher precision.
Rustam Stolkin - One of the best experts on this subject based on the ideXlab platform.
-
a novel selection Evolutionary Strategy for constrained optimization
Information Sciences, 2013Co-Authors: Licheng Jiao, Fang Liu, Ronghua Shang, Rustam StolkinAbstract:The existence of infeasible solutions makes it very difficult to handle constrained optimization problems (COPs) in a way that ensures efficient, optimal and constraint-satisfying convergence. Although further optimization from feasible solutions will typically lead in a direction that generates further feasible solutions, certain infeasible solutions can also provide useful information about the optimal direction of improvement for the objective function. How well an algorithm makes use of these two solutions determines its performance on COPs. This paper proposes a novel selection Evolutionary Strategy (NSES) for constrained optimization. A self-adaptive selection method is introduced to exploit both informative infeasible and feasible solutions from a perspective of combining feasibility with multi-objective problem (MOP) techniques. Since the global optimal solution of a COP is a feasible non-dominated solution, both non-dominated solutions with low constraint violation and feasible ones with low objective values are beneficial to an evolution process. Thus, the exploration and exploitation of both of these two kinds of solutions are preferred during the selection procedure. Several theorems and properties are given to prove the above assertion. Furthermore, the performance of our method is evaluated using 22 well-known benchmark functions. Experimental results show that the proposed method outperforms state-of-the-art algorithms in terms of the speed of finding feasible solutions and the stability of converging to global optimal solutions. In particular, when dealing with problems that have zero feasibility ratios and more than one active constraint, our method provides feasible solutions within fewer fitness evaluations (FES) and converges to the optimal solutions more reliably than other popular methods from the literature.
Douglas H Werner - One of the best experts on this subject based on the ideXlab platform.
-
improved electromagnetics optimization the covariance matrix adaptation Evolutionary Strategy
IEEE Antennas and Propagation Magazine, 2015Co-Authors: Micah D. Gregory, Spencer V Martin, Douglas H WernerAbstract:The covariance matrix adaptation Evolutionary Strategy (CMA-ES) is explored here as an improved alternative to well-established algorithms used in electromagnetic (EM) optimization. In the past, methods such as the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) have commonly been used for EM design. In this article, we examine and compare the performance of CMA-ES, PSO, and DE when applied to test functions and several challenging EM design problems. Of particular interest is demonstrating the ability of the relatively new CMA-ES to more quickly and more reliably find acceptable solutions compared with those of the more classical optimization strategies. In addition, it will be shown that due to its self-adaptive scheme, CMA-ES is a more user-friendly algorithm that requires less knowledge of the problem for preoptimization configuration.
-
design of ultra wideband aperiodic antenna arrays with the cma Evolutionary Strategy
IEEE Transactions on Antennas and Propagation, 2014Co-Authors: Philip J Gorman, Micah D. Gregory, Douglas H WernerAbstract:Recently, the covariance matrix adaption Evolutionary Strategy (CMA-ES) has received attention for outperforming conventional global optimization techniques such as the genetic algorithm (GA) or particle swarm optimization (PSO), often used in electromagnetic designs. Here, CMA-ES is first applied to the design of ultra-wideband aperiodic arrays using realistic spiral radiating elements. To improve the axial ratio of the array, optimization was extended to incorporate a mechanical rotation of each spiral element. This novel Strategy of optimizing both the location and rotation of each element provides noticeable improvement in both the axial ratio and sidelobe level performance.
-
Fast optimization of electromagnetic design problems using the covariance matrix adaptation Evolutionary Strategy
IEEE Transactions on Antennas and Propagation, 2011Co-Authors: Micah D. Gregory, Zikri Bayraktar, Douglas H WernerAbstract:A new method of optimization recently made popular in the Evolutionary computation (EC) community is introduced and applied to several electromagnetics design problems. First, a functional overview of the covariance matrix adaptation Evolutionary Strategy (CMA-ES) is provided. Then, CMA-ES is critiqued alongside a conventional particle swarm optimization (PSO) algorithm via the design of a wideband stacked-patch antenna. Finally, the two algorithms are employed for the design of small to moderate size aperiodic ultrawideband antenna array layouts (up to 100 elements). The results of the two electromagnetics design problems illustrate the ability of CMA-ES to provide a robust, fast and user-friendly alternative to more conventional optimization strategies such as PSO. Moreover, the ultrawideband array designs that were created using CMA-ES are seen to exhibit performances surpassing the best examples that have been reported in recent literature.
-
Next generation electromagnetic optimization with the covariance matrix adaptation Evolutionary Strategy
2011 IEEE International Symposium on Antennas and Propagation (APSURSI), 2011Co-Authors: Micah D. Gregory, Douglas H WernerAbstract:Classical Evolutionary strategies such as the genetic algorithm and particle swarm technique have long been the most called upon methods for optimization of electromagnetic design problems. Due to their capability for robust global search and their ease of implementation, they have been fruitfully applied to the design of antennas, arrays, frequency selective surfaces, metamaterials and other electromagnetic devices. Since then, many new optimization techniques have been developed that often allow more complex design problems to be tackled, or reduce the time needed to optimize the problems of the past. One algorithm found particularly effective is the covariance matrix adaptation Evolutionary Strategy (CMA-ES). The operation of CMA-ES will be covered in detail here. Additionally, the powerful performance of the technique when confronted with several different design problems and test functions will be demonstrated.
Morteza Gharib - One of the best experts on this subject based on the ideXlab platform.
-
experimental trajectory optimization of a flapping fin propulsor using an Evolutionary Strategy
Bioinspiration & Biomimetics, 2018Co-Authors: Nathan Martin, Morteza GharibAbstract:The experimental optimization of bio-inspired flapping fin trajectories are demonstrated for potential applications as a side or a rear propulsor of an autonomous underwater vehicle. The trajectories are scored based upon their difference from a force set-point and upon their efficiency and are parameterized by 10 variables inspired by fish swimming. The flapping fin is a generic rectangular rigid flat plate with a tapered edge. Optimization occurs as follows. First, a generation of trajectories is created. Second, the trajectories are executed by a spherical parallel manipulator, during which the forces are acquired. Third, the trajectories are scored and a new generation of trajectories is created using the covariance matrix adaptive Evolutionary Strategy. This loop repeats ad-infinitum until the search converges. Within the first set of searches, two trajectories for optimal side-force generation are found, one is fully three-dimensional while the other is artificially constrained to a line, and one trajectory for optimal thrust generation is found. All searches demonstrate good convergence properties and match the desired force set-point almost immediately. Additional generations primarily improve the efficiency of the maneuver. The two trajectories for generating side-force have a similar efficiency, which shows potential in utilizing a simple trajectory limited to a line. Comparison between the trajectories for generating side-force and thrust suggests that side-force generation is more efficient around Re ~1000, based on the average tip velocity and length of the fin. The second set of searches explores the behavior of the optimal trajectories for generating side-force at a lower force set-point and the third set of searches explores the sensitivity and repeatability of the optimization.
