Wild Gibbon Optimization Algorithm

Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also arise.To address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population behavior.WGOAcomprises two strategies:community search and community competition.The community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm diversity.Meanwhile,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm precision.To assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test functions.In the CEC2017 test suite,WGOA secures first place in 10 functions.In the CEC2022 benchmark functions,WGOA obtained the first rank in 5 functions.The ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested functions.This underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode.

Study on Gait Planning of Dynamic Walking of Biped Robots Based on Optimization Theory

In this paper, two important problems in the gait planning of dynamic walking of biped robot, i.e., finding inverse kinematic solution and constructing joint trajectories, are studied in detail by adopting complex optimization theory. The optimization algorithm for finding the inverse kinematic solution is developed, the construction method of joint trajectories is given, and the gait planning method of dynamic walking of biped robots is proposed.

SOLVING SYSTEMS OF PHASELESS EQUATIONS VIA RIEMANNIAN OPTIMIZATION WITH OPTIMAL SAMPLING COMPLEXITY

A Riemannian gradient descent algorithm and a truncated variant are presented to solve systems of phaseless equations|Ax|^(2)=y.The algorithms are developed by exploiting the inherent low rank structure of the problem based on the embedded manifold of rank-1 positive semidefinite matrices.Theoretical recovery guarantee has been established for the truncated variant,showing that the algorithm is able to achieve successful recovery when the number of equations is proportional to the number of unknowns.Two key ingredients in the analysis are the restricted well conditioned property and the restricted weak correlation property of the associated truncated linear operator.Empirical evaluations show that our algorithms are competitive with other state-of-the-art first order nonconvex approaches with provable guarantees.

Spatiotemporal Input Control:Leveraging Temporal Variation in Network Dynamics

The number of available control sources is a limiting factor to many network control tasks.A lack of input sources can result in compromised controllability and/or sub-optimal network performance,as noted in engineering applications such as the smart grids.The mechanism can be explained by a linear timeinvariant model,where structural controllability sets a lower bound on the number of required sources.Inspired by the ubiquity of time-varying topologies in the real world,we propose the strategy of spatiotemporal input control to overcome the source-related limit by exploiting temporal variation of the network topology.We theoretically prove that under this regime,the required number of sources can always be reduced to 2.It is further shown that the cost of control depends on two hyperparameters,the numbers of sources and intervals,in a trade-off fashion.As a demonstration,we achieve controllability over a complex network resembling the nervous system of Caenorhabditis elegans using as few as 6%of the sources predicted by a static control model.This example underlines the potential of utilizing topological variation in complex network control problems.

0N THE COMPLEXITY OF AN OPTIMAL ROUTING TREE PROBLEM

A routing tree for a set of tasks is a decision tree which assigns the tasks to their destinationsaccording to the features of the tasks. A weighted routing tree is one with costs attached to each linkof the tree. Links of the same feature have the same cost. It is proved that the problem of finding ?routing tree of the minimum cost for a given set of tasks of two features is NP-complete.

Adapting Nanotech Research as Nano-Micro Hybrids Approach Biological Complexity,A Review

Today＇s emergence of nano-micro hybrid structures with almost biological complexity is of fundamental interest. Our ability to adapt intelligently to the challenges has ramifications all the way from fundamentally changing research itself, over applications critical to future survival, to posing globally existential dangers. Touching on specific issues such as how complexity relates to the catalytic prowess of multi-metal compounds, we discuss the increasingly urgent issues in nanotechnology also very generally and guided by the motto ＇Bio Is Nature＇s Nanotech＇. Technology belongs to macro-evolution; for example integration with artificial intelligence （AI） is inevitable. Darwinian adaptation manifests as integration of complexity, and awareness of this helps in developing adaptable research methods that can find use across a wide range of research. The second half of this work reviews a diverse range of projects which all benefited from ＇playful＇ programming aimed at dealing with complexity. The main purpose of reviewing them is to show how such projects benefit from and fit in with the general, philosophical approach, proving the relevance of the ＇big picture＇ where it is usually disregarded.

Kernel Function-Based Primal-Dual Interior-Point Methods for Symmetric Cones Optimization

In this paper, we present a large-update primal-dual interior-point method for symmetric cone optimization（SCO） based on a new kernel function, which determines both search directions and the proximity measure between the iterate and the center path. The kernel function is neither a self-regular function nor the usual logarithmic kernel function. Besides, by using Euclidean Jordan algebraic techniques, we achieve the favorable iteration complexity O（ √r（1/2）（log r）^2 log（r/ ε））, which is as good as the convex quadratic semi-definite optimization analogue.

Cascadic multigrid methods for parabolic problems

In this paper,we consider the cascadic multigrid method for a parabolic type equation.Backward Euler approximation in time and linear finite element approximation in space are employed.A stability result is established under some conditions on the smoother.Using new and sharper estimates for the smoothers that reflect the precise dependence on the time step and the spatial mesh parameter,these conditions are verified for a number of popular smoothers.Optimal error bound sare derived for both smooth and non-smooth data.Iteration strategies guaranteeing both the optimal accuracy and the optimal complexity are presented.

Preparation and properties of chitosan–metal complex：Some factors influencing the adsorption capacity for dyes in aqueous solution

Chitosan–metal complexes have been widely studied in wastewater treatment, but there are still various factors in complex preparation which are collectively responsible for improving the adsorption capacity need to be further studied. Thus, this study investigates the factors affecting the adsorption ability of chitosan–metal complex adsorbents, including various kinds of metal centers, different metal salts and crosslinking degree. The results show that the chitosan–Fe（ Ⅲ） complex prepared by sulfate salts exhibited the best adsorption efficiency（100%） for various dyes in very short time duration（10 min）, and its maximum adsorption capacity achieved 349.22 mg/g. The anion of the metal salt which was used in preparation played an important role to enhance the adsorption ability of chitosan–metal complex. SO4^（2-） ions not only had the effect of crosslinking through electrostatic interaction with amine group of chitosan polymer, but also could facilitate the chelation of metal ions with chitosan polymer during the synthesis process.Additionally, the p H sensitivity and the sensitivity of ionic environment for chitosan–metal complex were analyzed. We hope that these factors affecting the adsorption of the chitosan–metal complex can help not only in optimizing its use but also in designing new chitosan–metal based complexes.

An agent-supported approach for the collaborative design of complex systems

A novel approach for partitioning and coordinating the collaborative design optimization of complex systems is described.A partitioning metric has been formulated to select the best partitioning solutions among the total possibilities of dividing the complex design optimization problem.Then,an agent-supported approach is used for the coordination of the collaborative design optimization.The approach has been applied to the case of a preliminary design of an electric vehicle,to demonstrate how various agents can effectively communicate with each other to provide support to the collaborative design optimization of complex systems.

Exploiting global information in complex network repair processes

Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger components at a low cost only with local information.In this paper,we discuss the effectiveness and efficiency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.