Ant colony algorithm based on genetic method for continuous optimization problem

A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.

Simultaneous optimization of even flow and land and timber value in forest planning:a continuous approach

Background:Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest.For this,many mathematical formulations have been proposed,both within the linear and non-linear programming frameworks,in the latter case generally considering integer variables in a combinatorial manner.We present a novel approach for planning the management of forests comprising single-species,even-aged stands,using a continuous,multi-objective formulation(considering economic and even flow)which can be solved with gradient-type methods.Results:The continuous formulation has proved robust in forest with different structures and different number of stands.The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems,both in terms of computational time(efficiency)and in the solution obtained(effectiveness).Their improvement increases drastically with the dimension of the problem(number of stands).Conclusions:It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models.The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques.The advantages of the new methodology are summarized as:it does not require to set management prescriptions in advance,it avoids the division of the planning horizon into periods,and it provides better solutions than the traditional combinatorial formulations.Additionally,the graphical display of trade-off information allows an a posteriori articulation of preferences in an intuitive way,therefore being a very interesting tool for the decision-making process in forest planning.

An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

Parameter design and performance analysis of zero inertia continuously variable transmission system

In order to solve the problem of weak power performance of vehicle equipped with continuously variable transmission(CVT) system working under transient operating conditions, a new CVT equipped with planetary gear mechanism and flywheel was researched, a design method of transmission parameter optimization was proposed, and the comprehensive matching control strategy was established for the new transmission system. Fuzzy controllers for throttle opening and CVT speed ratio were designed, and power performance and fuel economy of both vehicles respectively equipped with conventional CVT system and new transmission system wrere compared and analyzed by simulation. The results show that power performance and fuel economy of the vehicle equipped with new transmission system are better than that equipped with conventional CVT, thus the rationality of the parameter design method and control algorithm are verified.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

An Improved Elite Slime Mould Algorithm for Engineering Design

The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial performance.Therefore,this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems.For this aim,the structure of SMA is adjusted to develop the efficiency of the original method.As a stochastic optimizer,SMA mainly stimulates the behavior of slime mold in nature.For the harmony of the exploration and exploitation of SMA,the paper proposed an enhanced algorithm of SMA called ECSMA,in which two mechanisms are embedded into the structure:elite strategy,and chaotic stochastic strategy.The details of the original SMA and the two introduced strategies are given in this paper.Then,the advantages of the improved SMA through mechanism comparison,balance-diversity analysis,and contrasts with other counterparts are validated.The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA.Also,SMA is applied to four structural design issues of the welded beam design problem,PV design problem,I-beam design problem,and cantilever beam design problem with excellent results.

Optimization of strand and final electromagnetic stirrers of round bloom casters with multiple sections

Strand electromagnetic stirring（S-EMS） and final electromagnetic stirring（F-EMS） are the main methods used to improve the center porosity and segregation for round blooms. To optimize the stirring conditions, nail shooting tests were conducted for three sections of large round blooms with diameters of ф380 mm, ф450 mm, and ф600 mm. Acid leaching and sulfur print tests were used to investigate the shell thickness. Based on the results of nail shooting tests, a mathematical model of solidification was established, and the variation of shell thickness and the central solid fraction were exactly calculated by the model. By taking all sections into account, the locations of S-EMS and F-EMS were optimized for each section. In the results, the macro-segregation of various sections is improved after the locations of S-EMS and F-EMS systems are changed.

Investigation Effects of Selection Mechanisms for Gravitational Search Algorithm

The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.

Analyzing the Simple Ranking and Selection Process for Constrained Evolutionary Optimization

Many optimization problems that involve practical applications have functional constraints, and some of these constraints are active, meaning that they prevent any solution from improving the objective function value to the one that is better than any solution lying beyond the constraint limits. Therefore, the optimal solution usually lies on the boundary of the feasible region. In order to converge faster when solving such problems, a new ranking and selection scheme is introduced which exploits this feature of constrained problems. In conjunction with selection, a new crossover method is also presented based on three parents. When comparing the results of this new algorithm with six other evolutionary based methods, using 12 benchmark problems from the literature, it shows very encouraging performance. T-tests have been applied in this research to show if there is any statistically significance differences between the algorithms. A study has also been carried out in order to show the effect of each component of the proposed algorithm.

A Modified Integral Global Optimization Method and Its Asymptotic Convergence

In this paper, we propose a new integral global optimization algorithm for finding the solution of continuous minimization problem, and prove the asymptotic convergence of this algorithm. In our modified method we use variable measure integral, importance sampling and main idea of the cross-entropy method to ensure its convergence and efficiency. Numerical results show that the new method is very efficient in some challenging continuous global optimization problems.

A Convergent Algorithm for Continuously Optimal Location Problem

In this paper, the continuously optimal location problem is considered. The strong convexity of the objective function, the Lipschitz continuity of the gradient of the objective function are proved. Furthermore, a variant of conjugate gradient method for continuously optimal location problem is presented and its global convergence is analyzed.

Alternating Direction Method of Multipliers for Linear Programming

Linear programming is the core problem of various operational research problems.The dominant approaches for linear programming are simplex and interior point methods.In this paper,we showthat the alternating direction method of multipliers(ADMM),which was proposed long time ago while recently found more and more applications in a broad spectrum of areas,can also be easily used to solve the canonical linear programming model.The resulting per-iteration complexity is O(mn)where m is the constraint number and n the variable dimension.At each iteration,there are m subproblems that are eligible for parallel computation;each requiring only O(n)flops.There is no inner iteration as well.We thus introduce the newADMMapproach to linear programming,which may inspire deeper research for more complicated scenarios with more sophisticated results.

Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm

The Differential Evolution(DE)algorithm,which is an efficient optimization algorithm,has been used to solve various optimization problems.In this paper,adaptive dimensional learning with a tolerance framework for DE is proposed.The population is divided into an elite subpopulation,an ordinary subpopulation,and an inferior subpopulation according to the fitness values.The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population,respectively.The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy.If the global optimum is not improved in a specified number of iterations,a tolerance mechanism is applied.Under the tolerance mechanism,the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy,respectively.In addition,the individual status and algorithm status are used to adaptively adjust the control parameters.To evaluate the performance of the proposed algorithm,six state-of-the-art DE algorithm variants are compared on the benchmark functions.The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.