PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields

Resilient Bioinspired Algorithms: A Computer System Design Perspective

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Cotta, C., Olague, G. (2022). Resilient Bioinspired Algorithms: A Computer System Design Perspective. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_39Resilience can be defined as a system's capability for returning to normal operation after having suffered a disruption. This notion is of the foremost interest in many areas, in particular engineering. We argue in this position paper that is is a crucial property for bioinspired optimization algorithms as well. Following a computer system perspective, we correlate some of the defining requirements for attaining resilient systems to issues, features, and mechanisms of these techniques. It is shown that bioinspired algorithms do not only exhibit a notorious built-in resilience, but that their plasticity also allows accommodating components that may boost it in different ways. We also provide some relevant research directions in this area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

CELLULAR ORGANISM BASED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR COMPLEX NON-LINEAR PROBLEMS

Particle Swarm Optimization (PSO) is the global optimization technique that inspires many researchers to solve large scale of non-linear optimization problems. For certain complex scenarios, the premature convergence problem of PSO algorithm cannot find global optimum in dynamic environments. In this paper, a new variant motility factor based Cellular Particle Swarm Optimization (m-CPSO) algorithm is proposed which is developed by the migration behavior observed from fibroblast cellular organism to overcome this problem. The proposed m-CPSO algorithm is modeled in two different social best and individual best models. The performance of m-CPSO is tested in the benchmark and real-time data instances and compared with classical PSO. The outcome of experimental results has demonstrated that m-CPSO algorithm produces promising results than classical PSO on all evaluated environments

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

A simple island model with λλ islands and migration occurring after every ττ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+λ)(1+λ) EA if τ=1τ=1 , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to λ=O(n1−ϵ)λ=O(n1−ϵ) , the (1+λ)(1+λ) EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic λλ . The relationship of τ,λτ,λ , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology

A Novel Parametric benchmark generator for dynamic multimodal optimization

In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical aspects of DMMO problems. This study proposes the Deterministic Distortion and Rotation Benchmark (DDRB), a method to generate deterministic DMMO test problems that can simulate more diverse types of challenges when compared to existing benchmark generators for DMMO. DDRB allows for controlling the intensity of each type of challenge independently, enabling the user to pinpoint the pros and cons of a DMMO method. DDRB first develops an existing approach for generation of static multimodal functions in which the difficulty of global optimization can be controlled. Then, it proposes a scaling function to dynamically change the relative distribution, shapes, and sizes of the basins. A deterministic technique to control the regularity of the pattern in the change is also proposed. Using these components, a parametric test suite consisting of ten test problems is developed for DMMO. Mean Robust Peak Ratio for measuring the performance of DMMO methods is formulated to overcome the sensitivity of the conventional peak ratio indicator to the predefined threshold and niche radius. Numerical results of a successful multimodal optimization method, when augmented with a simple strategy to utilize previous information, are provided on the proposed test problems in different scenarios with the aim of serving as a reference for future studies

Analysing the police patrol routing problem : a review

Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP

The Impact of a Sparse Migration Topology on the Runtime of Island Models in Dynamic Optimization

Island models denote a distributed system of evolutionary algorithms which operate independently, but occasionally share their solutions with each other along the so-called migration topology. We investigate the impact of the migration topology by introducing a simplified island model with behavior similar to (Formula presented.) islands optimizing the so-called Maze fitness function (Kötzing and Molter in Proceedings of parallel problem solving from nature (PPSN XII), Springer, Berlin, pp 113–122, 2012). Previous work has shown that when a complete migration topology is used, migration must not occur too frequently, nor too soon before the optimum changes, to track the optimum of the Maze function. We show that using a sparse migration topology alleviates these restrictions. More specifically, we prove that there exist choices of model parameters for which using a unidirectional ring of logarithmic diameter as the migration topology allows the model to track the oscillating optimum through nMaze-like phases with high probability, while using any graph of diameter less than (Formula presented.) for some sufficiently small constant (Formula presented.) results in the island model losing track of the optimum with overwhelming probability. Experimentally, we show that very frequent migration on a ring topology is not an effective diversity mechanism, while a lower migration rate allows the ring topology to track the optimum for a wider range of oscillation patterns. When migration occurs only rarely, we prove that dense migration topologies of small diameter may be advantageous. Combined, our results show that the sparse migration topology is able to track the optimum through a wider range of oscillation patterns, and cope with a wider range of migration frequencies

Un marco de trabajo para las medidas de rendimiento en la optimización dinámica evolutiva

Varios fenómenos reales pueden ser modelados como problemas dinámicos de optimización.  Estos problemas han sido tratados eficientemente mediante métodos evolutivos durante los últimos 25 años. En  este  contexto, la evaluación del rendimiento de estos métodos es aún un tema  en desarrollo. Sin embargo, a partir de una revisión de la literatura  desarrollada en este trabajo, es posible advertir la ausencia de un marco de trabajo que se organiza convenientemente los progresos alcanzados en este campo de investigación. En consecuencia, la presente investigación tiene como objetivo proponer un marco de trabajo que permita no solo organizar los avances actuales, sino también identificar posibles medidas aún no propuestas. Se incluye además un análisis de las principales tendencias en este campo de investigación. El principal resultado obtenido a partir del marco de trabajo propuesto es el predominio de medidas de rendimiento basadas en el promedio de la calidad de la solucion obtenida por el algoritmo en términos de la funcion objetivo.  Keywords:   Medidas  de rendimiento, Optimización dinámica  evolutiva,  Evaluación de algoritmos

Optimization of non-stationary Stackelberg models using a self-adaptive evolutionary algorithm

Los modelos de Juegos de Stackelberg engloban una importante familia de problemas de la Teoría de Juegos, que encuentra aplicaciones directas en economía. El principal objetivo es encontrar un equilibrio óptimo entre las decisiones que pueden tomar dos actores que se relacionan jerárquicamente. En general estos modelos son complejos de resolver dada su estructura jerárquica, y la frecuente aparición en estos de funciones objetivos o restricciones intratables analíticamente. Otra causa de dicha complejidad es la existencia de incertidumbre, particularmente debido a la variabilidad en el tiempo de las condiciones del mercado, estrategias de los competidores, entre otras. Un análisis de la literatura relacionada muestra muy pocos trabajos abordando estos problemas de optimización no estacionarios. En este sentido, la presente investigación propone una técnica meta-heurística auto-adaptativa para resolver modelos de Juegos de Stackelberg no estacionarios. Los resultados experimentales obtenidos muestran una mejoría significativa sobre un método existente.Stackelberg’s game models involve an important family of Game Theory problems with direct application on economics scenarios. Their main goal is to find an optimal equilibrium between the decisions from two actors that are related one to each other hierarchically. In general, these models are complex to solve due to their hierarchical structure and intractability from an analytical viewpoint. Another reason for such a complexity comes from the presence of uncertainty, which often occurs because of the variability over time of market conditions, adversary strategies, among others aspects. Despite their importance, related literature reflects a few works addressing this kind of non-stationary optimization problems. So, in order to contribute to this research area, the present work proposes a self-adaptive meta-heuristic method for solving online Stackelberg’s games. Experiment results show a significant improvement over an existing method