Holographic research strategy (HRS) is a novel determinate optimization method.The principle of HRS is based on a special, two-dimensional presentation of a multidimensional space.This presentation was termed two-dime...Holographic research strategy (HRS) is a novel determinate optimization method.The principle of HRS is based on a special, two-dimensional presentation of a multidimensional space.This presentation was termed two-dimensional hologram.HRS translated the optimization operation in multidimensional space into finding better points in the neighborhood around the current best data points.In this way, HRS can find the global optimal parameters in all probability.However, HRS can’t be applied to optimize continuous variables, it was only used in optimizing discrete systems.Therefore, it is necessary of improving HRS and ensuring the optimization algorithm can being applied in multidimensional continuous systems.Modified holographic research strategy (MHRS) was designed for the purpose.MHRS changed continuous variables into discrete variables in the searching region firstly, and then found the optimum in the discrete system.In order to reduce the deviation between the continuous system and the discrete system, MHRS adopted iterative algorithm to shrink the searching region gradually according to the location of the current optimal value.Furthermore, in order to improve the efficiency of HRS in searching for the global optimum, random mutation operator was added to the optimizing process.Ten-dimensional Rastrigin function was applied to testing MHRS, the results demonstrated that its global optimization performance [JP+5]is superior [JP+4]to one of eugenic evolution genetic algorithm (EGA).Further, MHRS was applied to estimate the kinetic model parameters of residue hydrofining.Satisfactory results were obtained.展开更多
受生物蚂蚁觅食行为的启发,拓展蚁群系统的性能,以正态分布模拟信息素的密度分布,并以此进行随机数抽样,构成蚁群的状态转移规则。系统将随着蚂蚁的移动调整分布函数,实施信息素更新,蚁群在信息素的引导下逐步向最优食物源聚集。系统还...受生物蚂蚁觅食行为的启发,拓展蚁群系统的性能,以正态分布模拟信息素的密度分布,并以此进行随机数抽样,构成蚁群的状态转移规则。系统将随着蚂蚁的移动调整分布函数,实施信息素更新,蚁群在信息素的引导下逐步向最优食物源聚集。系统还引入优进策略和变异策略,加强局部挖掘和全局探索机制,提高蚁群的寻优能力,构建为混合连续蚁群系统(hybrid continuous ant colony system,HCACS)。经多种经典函数的测试,表明HCACS适用于连续优化问题,性能良好,对于维数较高和搜索空间较宽广的问题,更具优势。HCACS算法的参数较少,设置简单,实用性较强。展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired comp...The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum(minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization(SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.展开更多
Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and b...Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and benefits of complexity. Here we investigate this trade-off for an animal that has to decide whether or not to forage for food - and so how much energy reserves to store - depending on the food availability in its environment. We evolve a decision rule that controls the target reserve level for different ranges of food availability, but where increasing complexity is costly in that metabolic rate increases with the sensitivity of the rule. The evolved rule tends to be much less complex than the optimal strategy but performs almost as well, while being less costly to implement. It achieves this by being highly sensitive to changing food availability at low food abun- dance - where it provides a close fit to the optimal strategy - but insensitive when food is plentiful. When food availability is high, the target reserve level that evolves is much higher than under the optimal strategy, which has implications for our under- standing of obesity. Our work highlights the important principle of generalisability of simple decision-making mechanisms, which enables animals to respond reasonably well to conditions not directly experienced by themselves or their ancestors.展开更多
文摘Holographic research strategy (HRS) is a novel determinate optimization method.The principle of HRS is based on a special, two-dimensional presentation of a multidimensional space.This presentation was termed two-dimensional hologram.HRS translated the optimization operation in multidimensional space into finding better points in the neighborhood around the current best data points.In this way, HRS can find the global optimal parameters in all probability.However, HRS can’t be applied to optimize continuous variables, it was only used in optimizing discrete systems.Therefore, it is necessary of improving HRS and ensuring the optimization algorithm can being applied in multidimensional continuous systems.Modified holographic research strategy (MHRS) was designed for the purpose.MHRS changed continuous variables into discrete variables in the searching region firstly, and then found the optimum in the discrete system.In order to reduce the deviation between the continuous system and the discrete system, MHRS adopted iterative algorithm to shrink the searching region gradually according to the location of the current optimal value.Furthermore, in order to improve the efficiency of HRS in searching for the global optimum, random mutation operator was added to the optimizing process.Ten-dimensional Rastrigin function was applied to testing MHRS, the results demonstrated that its global optimization performance [JP+5]is superior [JP+4]to one of eugenic evolution genetic algorithm (EGA).Further, MHRS was applied to estimate the kinetic model parameters of residue hydrofining.Satisfactory results were obtained.
文摘受生物蚂蚁觅食行为的启发,拓展蚁群系统的性能,以正态分布模拟信息素的密度分布,并以此进行随机数抽样,构成蚁群的状态转移规则。系统将随着蚂蚁的移动调整分布函数,实施信息素更新,蚁群在信息素的引导下逐步向最优食物源聚集。系统还引入优进策略和变异策略,加强局部挖掘和全局探索机制,提高蚁群的寻优能力,构建为混合连续蚁群系统(hybrid continuous ant colony system,HCACS)。经多种经典函数的测试,表明HCACS适用于连续优化问题,性能良好,对于维数较高和搜索空间较宽广的问题,更具优势。HCACS算法的参数较少,设置简单,实用性较强。
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
文摘The evolutionary algorithm, a subset of computational intelligence techniques, is a generic population-based stochastic optimization algorithm which uses a mechanism motivated by biological concepts. Bio-inspired computing can implement successful optimization methods and adaptation approaches, which are inspired by the natural evolution and collective behavior observed in species, respectively. Although all the meta-heuristic algorithms have different inspirational sources, their objective is to find the optimum(minimum or maximum), which is problem-specific. We propose and evaluate a novel synergistic fibroblast optimization(SFO) algorithm, which exhibits the behavior of a fibroblast cellular organism in the dermal wound-healing process. Various characteristics of benchmark suites are applied to validate the robustness, reliability, generalization, and comprehensibility of SFO in diverse and complex situations. The encouraging results suggest that the collaborative and self-adaptive behaviors of fibroblasts have intellectually found the optimum solution with several different features that can improve the effectiveness of optimization strategies for solving non-linear complicated problems.
文摘Models of adaptive behaviour typically assume that animals behave as though they have highly complex, detailed strategies for making decisions. In reality, selection favours the optimal balance between the costs and benefits of complexity. Here we investigate this trade-off for an animal that has to decide whether or not to forage for food - and so how much energy reserves to store - depending on the food availability in its environment. We evolve a decision rule that controls the target reserve level for different ranges of food availability, but where increasing complexity is costly in that metabolic rate increases with the sensitivity of the rule. The evolved rule tends to be much less complex than the optimal strategy but performs almost as well, while being less costly to implement. It achieves this by being highly sensitive to changing food availability at low food abun- dance - where it provides a close fit to the optimal strategy - but insensitive when food is plentiful. When food availability is high, the target reserve level that evolves is much higher than under the optimal strategy, which has implications for our under- standing of obesity. Our work highlights the important principle of generalisability of simple decision-making mechanisms, which enables animals to respond reasonably well to conditions not directly experienced by themselves or their ancestors.
