Enrique Raúl Villa Diharce

Constrained optimization with an improved particle swarm optimization algorithm

Purpose – The purpose of this paper is to present a new constrained optimization algorithm based on a particle swarm optimization (PSO) algorithm approach.Design/methodology/approach – This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. A constraint handling technique based on feasibility and sum of constraints violation is adopted. Also, a special technique to handle equality constraints is proposed.Findings – The paper shows that it is possible to improve PSO and keeping the advantages of its social interaction through a simple idea: perturbing the PSO memory.Research limitations/implications – The proposed algorithm shows a competitive performance against the state‐of‐the‐art constrained optimization algorithms.Practical implications – The proposed algorithm can be used to solve single objective problems with linear or non‐linear functions, and subject to both equality and inequality constraints which can be linea...

Continuous Constrained Optimization with Dynamic Tolerance Using the COPSO Algorithm

This work introduces a hybrid PSO algorithm which includes perturbation operators to keep population diversity. A new neighborhood structure for Particle Swarm Optimization called Singly-Linked Ring is implemented. The approach proposes a neighborhood similar to the ring structure, but which has an innovative neighbors selection. The objective is to avoid the premature convergence into local optimum. A special technique to handle equality constraints with low side effects on the diversity is the main feature of this contribution. Two perturbation operators are used to improve the exploration, applying the modification only in the particle best population.We show through a number of experiments how, by keeping the selection pressure on a decreasing fraction of the population, COPSO can consistently solve a benchmark of constrained optimization problems.

Particle evolutionary swarm for design reliability optimization

This papers proposes an enhanced Particle Swarm Optimization algorithm with multi-objective optimization concepts to handle constraints, and operators to keep diversity and exploration. Our approach, PESDRO, is found robust at solving redundancy and reliability allocation problems with two objective functions: reliability and cost. The approach uses redundancy of components, diversity of suppliers, and incorporates a new concept called Distribution Optimization. The goal is the optimal design for reliability of coherent systems. The new technique is compared against algorithms representative of the state-of-the-art in the area by using a well-known benchmark. The experiments indicate that the proposed approach matches and often outperforms such methods.

Robust PSO-Based Constrained Optimization by Perturbing the Particle's Memory

A successful evolutionary algorithm is one with the proper balance between exploration (searching for good solutions), and exploitation (refining the solutions by combining information gathered during the exploration phase). Diversity maintenance is important in constrained search space algorithms because the additional pressure set on the population to reach the feasible region reduces the diversity. Since reduced diversity promotes premature convergence, new exploration and exploitation techniques have been incorporated into the PSO main paradigm. In this chapter the authors review the standard PSO algorithm, and several proposals to improve both exploration and exploitation: local and global topologies, particle motion equations, swarm neighbourhoods, and interaction models. For all these approaches the common shared feature is the modification of the PSO main algorithm. The present chapter, however, describes a rather different approach: the perturbation of the particle memory. In the PSO algorithm, the next particle’s position is based on their flying experience (

The singly-linked ring topology for the particle swarm optimization algorithm

This paper introduces a new neighborhood structure for Particle Swarm Optimization, called Singly-Linked Ring. The approach proposes a neighborhood whose members share the information at a different rate. The objective is to avoid the premature convergence of the flock and stagnation into local optimal. The approach is applied at a set of global optimization problems commonly used in the literature. The singly-linked structure is compared against the state-of-the-art neighborhoods structures. The proposal is easy to implement, and its results and its convergence performance are better than other structures.

The Gaussian Polytree EDA with Copula Functions and Mutations

This chapter introduces the Gaussian Poly-Tree Estimation Distribution Algorithm, and two extensions: i) with Gaussian copula functions, and ii) with local optimizers. The new construction and simulation algorithms, and its application to estimation of distribution algorithms with continuous Gaussian variables are also introduced. The algorithm for the construction of the structure and for edge orientation is based on information theoretic concepts such as mutual information and conditional mutual information. The three models are tested on a benchmark of 20 unimodal and multimodal functions. The version with copula function and mutations excels in most problems achieving near optimal success rate.

Particle evolutionary swarm optimization with linearly decreasing ε-tolerance

We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two perturbation operators: “c-perturbation” and “m-perturbation”. The goal of these operators is to prevent premature convergence and the poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules, enhanced with a dynamic e-tolerance approach applicable to equality constraints. PESO is compared and outperforms highly competitive algorithms representative of the state of the art.

Global optimization with the gaussian polytree EDA

This paper introduces the Gaussian polytree estimation of distribution algorithm, a new construction method, and its application to estimation of distribution algorithms in continuous variables. The variables are assumed to be Gaussian. The construction of the tree and the edges orientation algorithm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of benchmark functions. The experimental results show that the approach is robust, comparisons are provided.

A Takagi–Sugeno-Kang Fuzzy Model Formalization of Eelgrass Leaf Biomass Allometry with Application to the Estimation of Average Biomass of Leaves in Shoots: Comparing the Reproducibility Strength of the Present Fuzzy and Related Crisp Proxies

The identification of the functional relationship that regulates the variation of individual leaf biomass in terms of related area in eelgrass, allows the derivation of convenient proxies for a nondestructive estimation of the average biomass of the leaves in shoots. The concourse of these assessment methods is fundamental for assessing the performance of restoration efforts for this species that are based on transplanting techniques. Prior developments proposed proxies for a nondestructive estimation of aforementioned average biomass of leaves in shoots derived from allometric models for the dependence of leaf biomass in terms of linked area. The reproducibility power of these methods is highly dependent on analysis method and data quality. Indeed, previous results show that allometric proxies for average biomass of leaves in shoots produced by parameter estimates fitted from quality controlled data via nonlinear regression yield the highest reproducibility strength. Nevertheless, the use of data processing entails subtleties mainly related to the subjectivity of the criteria for the rejection of inconsistent replicates in raw data. Here we introduce efficient- data quality control- free surrogates derived from a first order Takagi-Sugeno-Kang fuzzy model aimed to approximate the mean response of eelgrass leaf biomass depending on associated area. A comparison of the performances of the allometric and the fuzzy model constructs identified using available raw data shows that the Takagi-Sugeno-Kang paradigm for individual leaf biomass in terms of related area produced the most precise proxies for observed average biomass of leaves in shoots. The present results show how gains derived from the outstanding approximation capabilities of the first order Takagi-Sugeno-Kang fuzzy model for the nonlinear dynamics can be extended to the realm of eelgrass allometry.

Constraint handling techniques for a non-parametric real-valued estimation distribution algorithm

This article introduces the Non-Parametric Real-valued Estimation Distribution Algorithm (NOPREDA), and its application to constrained optimization problems. NOPREDA approximates the target probability density function by building the cumulative empirical distribution of the decision variables. Relationships and structure among the data is modeled through a rank correlation matrix (Spearmans statistics). The procedure to induce a target rank correlation matrix into the new population is described. NOPREDA is used to solve constrained optimization problems. Three constraint handling techniques are investigated: truncation selection, feasibility tournament, and Stochastic Ranking. NOPREDAs performance is competitive in problems with inequality constraints. However, a mechanism for properly handling equality constraints remains as part of our future research work.