Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

Many optimization problems admit a number of local optima, among which there is the global optimum. For these problems, various heuristic optimization methods have been proposed. Comparing the results of these solvers requires the definition of suitable metrics. In the electrical energy systems literature, simple metrics such as best value obtained, the mean value, the median or the standard deviation of the solutions are still used. However, the comparisons carried out with these metrics are rather weak, and on these bases a somehow uncontrolled proliferation of heuristic solvers is taking place. This paper addresses the overall issue of understanding the reasons of this proliferation, showing a conceptual scheme that indicates how the assessment of the best solver may result in the unlimited formulation of new solvers. Moreover, this paper shows how the use of more refined metrics defined to compare the optimization result, associated with the definition of appropriate benchmarks, may make the comparisons among the solvers more robust. The proposed metrics are based on the concept of first-order stochastic dominance and are defined for the cases in which: (i) the globally optimal solution can be found (for testing purposes); and (ii) the number of possible solutions is so large that practically it cannot be guaranteed that the global optimum has been found. Illustrative examples are provided for a typical problem in the electrical energy systems area – distribution network reconfiguration. The conceptual results obtained are generally valid to compare the results of other optimization problem

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

The main objective of thermoelectric power plants is to meet the power demand with the lowest fuel cost and emission levels of pollutant and greenhouse gas emissions, considering the operational restrictions of the power plant. Optimization techniques have been widely used to solve engineering problems as in this case with the objective of minimizing the cost and the pollution damages. Heuristic and metaheuristic algorithms have been extensively studied and used to successfully solve this multi-objective problem. This chapter, several optimization techniques (simulated annealing, ant lion, dragonfly, NSGA II, and differential evolution) are analyzed and their application to economic-emission load dispatch (EELD) is also discussed. In addition, a comparison of all approaches and its results are offered through a case study

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

A Multiple-Objective Framework for Sustainable Forest Management under Uncertainty in the U.S. Central Hardwood Region

Despite the economic and ecological significance of oak-hickory forests in the Central Hardwood Region (CHR), major challenges are faced by both private and public landowners and policymakers due to the lack of reliable growth and yield models as well as the absence of useful tools for multi-criteria management. Moreover, the effects of climate change and fire disturbance on these forests and their management are largely unknown.;The second chapter of the dissertation is directed towards the study of the community and population structure of CHR forests under climate change and associated changes of fire regimes. The Central Hardwood Region of the United States constitutes one of the most diverse ecoregions in North America and the most extensive temperate deciduous forest in the world. Despite the economic and ecological significance of the CHR, the long term effects of changes in climate and fire regime on forest structures remain largely unknown. In this study, we developed an integrated climate sensitive matrix framework to synchronously couple (1) forest dynamics, (2) mean fire interval, (3) population density, and (4) future climate scenarios to study the community and population structure of CHR forests under climate change and associated changes of fire regimes. Using Monte Carlo simulations and coupled forest dynamics-disturbance models, we projected that the CHR would undergo a major shift in population structure from the present to year 2100. The fundamental changes would consist of a transition of dominant species from oak and hickory to maple species, reduced species diversity, and substantial declines in stand basal area and stand volume compared to year 2010. These projected changes may have profound ecological and economic implications. Ecologically, changes in tree species diversity favoring maples would alter ecosystem processing of nutrients and subsequent nutrient flows to drainage waters within the region. Habitat change would alter the broad spectrum of organisms relying on the forest, leading to a redistribution of wildlife species, further heightening the risks for endangered species. On the brink of these fundamental shifts, our study calls for ecologically and economically informed conservation and mitigation strategies to better prepare society for the associated changes in ecosystem services and economic benefits derivable from the CHR forests.;The third chapter further addresses assessments of management impacts on central hardwood forests under climate and fire uncertainty. Central hardwood forests, in the absence of management, are predicted to undergo a species shift and decline in stocks due to climate change and increased fire frequencies. Here I quantified how various management intensities would influence these forests in terms of the net present value (NPV) of harvests, tree species and size diversity, and carbon stocks in four pools: above-ground biomass, fine roots, dead organic matters, and soil. An uncertainty analysis with fuzzy sets shows that when considering uncertain climate and fire, the NPV, size diversity, and total carbon stock would be distinctively different in climate scenarios RCP2.6 and RCP8.5 with high certainty. However, for species diversity, similar climatic effects on species diversity may exist across most management regimes.;The fourth chapter focused on modeling multi-stage scenario-based optimization under uncertainty in climate-induced fire disturbance. I developed multi-stage scenario-based optimization models for managing central hardwood forests under uncertainty in climate change and associated fire regimes. Based on a climate-sensitive matrix growth model and a mean fire interval model, four future climate scenarios and attendant fire intervals combined with two fire severity regimes were transformed into 36 and 20 tree growth scenarios for harvesting cycles of 10 and 20 years, respectively. Three alternatives of optimization formulations were proposed: 1) optimize for the maximum objective value under each individual scenario independently; 2) based on results from (1), find the compromise management plan that\u27s feasible for all scenarios while minimizing the weighted sum of deviations between the realized and maximum objective values; and 3) derive the optimal management plan over the entire scenario tree. Four objectives were considered: the net present value (NPV) of harvests, total carbon stock, tree species diversity, and tree size diversity. Finally I determined the trade-off between economic and ecological benefits by quantifying the opportunity cost of increasing ecological benefits in terms of NPV. (Abstract shortened by ProQuest.)