Chaotic optimization algorithm based on the modified probability density function of Lozi map

Chaos optimization algorithms (COAs) usually utilize different chaotic maps(logistic, tent, Hénon, Lozi,...) to generate the pseudo-random numbers mapped as the design variables for global optimization. In this paper we are going to propose new technique to improve the chaotic optimization algorithm by using some transformations to modify the density of the map instead of changing it.

Numerical simulation of multi output radar orthogonal waveform based on chaos optimization algorithm

Aiming at the problems of low probability of interception and poor anti-jamming performance of multi output radar, the numerical simulation of orthogonal waveform of multi output radar based on chaos optimization algorithm is proposed. Firstly, chaotic frequency coding is applied to multi output radar signal, and different frequency modulation is applied to different sub pulse. At the same time, in view of the low efficiency of numerical simulation algorithm in large space and high dimension optimization, GASA algorithm is used to increase the diversity of chaotic optimization algorithm process. According to the specific working mode, the initial phase of each cycle of multi output radar orthogonal waveform is obtained, and the number of numerical simulation of multi output radar orthogonal waveform is established Model. The experimental results show that the proposed method can improve the radar energy utilization and the signal-to-noise ratio of the echo signal, allocate the transmitting energy reasonably, and keep the structural stability of LFM signal frequency changing continuously with time.

Multiobjective Parallel Chaos Optimization Algorithm with Crossover and Merging Operation

Chaos optimization algorithm (COA) usually utilizes chaotic maps to generate the pseudorandom numbers mapped as the decision variables for global optimization problems. Recently, COA has been applied to many single objective optimization problems and simulations results have demonstrated its effectiveness. In this paper, a novel parallel chaos optimization algorithm (PCOA) will be proposed for multiobjective optimization problems (MOOPs). As an improvement to COA, the PCOA is a kind of population-based optimization algorithm which not only detracts the sensitivity of initial values but also adjusts itself suitable for MOOPs. In the proposed PCOA, crossover and merging operation will be applied to exchange information between parallel solutions and produce new potential solutions, which can enhance the global and fast search ability of the proposed algorithm. To test the performance of the PCOA, it is simulated with several benchmark functions for MOOPs and mixed H2/H∞ controller design. The simulation results show that PCOA is an alternative approach for MOOPs.

An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms

The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency.

Conscious AI at the Edge of Chaos

The main problem for AI consciousness is to operate within the right kind of AI. We distinguish between the traditional computing (GOFAI), and the computing based on stochastic pattern optimization. The latter will be called here computing at the edge of chaos. Optimization of learning patterns, which is the gist of its success, often happens between the areas of too much repetitive order and those of hard to predict and control stochastic processes. This is to change the focus from the opposition of symbolic versus sub-symbolic computing; symbols can appear at different granularities and the hedge between The Physical Symbol System Hypothesis and neural nets seems no longer the most productive cut to make. Computing at the edge of chaos is promising for AGI, especially for AGI consciousness. The second problem for AI consciousness is to work with the right definitions of consciousness.