Steady-state Genetic Algorithm Research Articles

Radar placement optimization based on adaptive multi-objective meta-heuristics

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%.

A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

A Steady-State Genetic Algorithm for the Single Machine Scheduling Problem with Periodic Machine Availability

Research on the Access Planning of SOP and ESS in Distribution Network Based on SOCP-SSGA

This paper proposes a two-stage planning model for soft open point (SOP) and energy storage system (ESS) that considers the cost of faults in response to the current issue of SOP and ESS systems not considering the impact of SOP access on load transfer in the event of a fault in the distribution network. Firstly, considering the uncertainty of “PV-load”, typical scenarios of PV and load are constructed based on the clustering algorithm. Secondly, aiming at the economic performance of the distribution network and the capacity of PV access, a two-stage optimization model is established for the joint integration of SOP and ESS into the distribution network (normal and fault operation) under typical scenarios. The model is solved by using the second-order cone programming algorithm and steady-state genetic algorithm (SOCP-SSGA). Stage one involves planning for the integration capacity and location of SOP and ESS into the distribution network under each scenario within a period based on SOCP with the goal of minimizing economic costs. In stage two, the PV access capacity of the distribution network is optimized using SSGA with the goal of enhancing the PV accommodation capability. Finally, verification and analysis are conducted on an improved IEEE33 node system. The results show that when the system optimizes access to a group of SOP and ESS, the total economic cost is reduced by RMB 61,729 compared to random access, and the accessible PV capacity is increased by 0.5278 MW. Moreover, optimizing access to two sets of SOP and ESS can further reduce the total economic cost by RMB 107,048 compared to the optimized access group and increase accessible PV capacity by 1.5751 MW. Therefore, the proposed plan for SOP and ESS planning in this paper can significantly reduce the economic cost of distribution networks, enhance the absorption capacity of distributed photovoltaics, improve the voltage level of power grid operation, and, thereby, improve the economic and reliability of distribution network operation.

Co-Optimizing CPU Voltage, Memory Placement, and Task Offloading for Energy-Efficient Mobile Systems

Energy-saving is one of the most important missions in the design of battery-based mobile systems. Many ideas have been suggested for saving energy in different system layers. Specifically, (1) lowering the supply voltage for idle CPU time slots, (2) using hybrid memory to save DRAM refresh power, and (3) task offloading to edge/cloud servers are well-acknowledged techniques used in CPU, memory, and network subsystems. In this paper, we show that co-optimizing these three techniques is necessary for further reducing the energy consumption of mobile real-time systems. To this end, we present an extended task model and formulate the effect of dynamic voltage/frequency scaling (DVFS), hybrid memory allocation, and task offloading problems as a unified measure. We then present a new real-time task scheduling scheme, called Co-TOMS, to co-optimize the energy-saving techniques in CPU, memory, and network subsystems by considering the given task set and resource conditions. The main contributions of our study can be summarized as follows. First, we optimize three energy-saving techniques across different system layers and find that they have significant influence on each other. For example, the effect of DVFS alone is limited in mobile systems, but combining it with offloading greatly amplifies its efficiency. Second, previous studies on offloading usually define a deadline as the maximum allowable latency at the application-level, but we focus on hard real-time systems that must meet task-level deadlines. Third, we design a steady-state genetic algorithm that allows fast convergence with reasonable computation overhead under various resource and workload conditions.

ІНФОРМАЦІЙНА СИСТЕМА ДЛЯ ЗАВАНТАЖЕННЯ КОНТЕЙНЕРНОГО СУДНА З УРАХУВАННЯМ СТРУКТУРНИХ ТА ОПЕРАЦІЙНИХ ОБМЕЖЕНЬ

Increasing the efficiency of loading container ships, especially in wartime, is one of the most important problems. The paper examines elimination of safety issues in marine transportation of containerized goods at the stowage planning stage. The article examines well-known stowage planning software that was developed in order to help with the stowage planning process. This paper presents a Boolean mathematical model of integer linear programming, which considers structural and operational constraints of a vessel and containers likewise, including hazardous cargoes compatibility constraints (according to the International Maritime Dangerous Cargoes Code) and container loading sequences (that depends on discharging port order). The proposed approach for solving the task mentioned above consists of dividing it into two stages. At the first stage, a preliminary stowage arrangement is calculated using a simple heuristic, which takes into account structural and operational limitations, container loading sequence and compatibility of dangerous cargoes as well as certain safety parameters (stability, durability, etc.). At the second stage, the arrangement is optimized using the safety parameters using a steady-state genetic algorithm. Developments on the subject include - data structures for application in container vessel stowage planning software; - interface that allows a user to graphically display a stowage plan of a container ship; - interface that allows a user to display safety parameters of a container ship (stability, durability, etc.); - interface that allows to input and edit containers and vessel data, as well as dangerous goods segregation provisions according to the International Maritime Dangerous Cargoes Code, and displays a notification if those are not satisfied.

Application of Modified Steady-State Genetic Algorithm for Batch Sizing and Scheduling Problem with Limited Buffers

Batch sizing and scheduling problems are usually tough to solve because they seek solutions in a vast combinatorial space of possible solutions. This research aimed to test and further develop a scheduling method based on a modified steady-state genetic algorithm and test its performance, in both the speed (low computational time) and quality of the final results as low makespan values. This paper explores the problem of determining the order and size of the product batches in a hybrid flow shop with a limited buffer according to the problem that is faced in real-life. Another goal of this research was to develop a new reliable software/computer program tool in c# that can also be used in production, and as result, obtain a flexible software solution for further research. In all of the optimizations, the initial population of the genetic algorithm was randomly generated. The quality of the obtained results, and the short computation time, together with the flexibility of the genetic paradigm prove the effectiveness of the proposed algorithm and method to solve this problem.

Metaheuristics for the permutation flowshop problem with a weighted quadratic tardiness objective

In this paper, we consider a permutation flowshop problem, with a weighted squared tardiness objective function, which addresses an important criterion for many customers. Our objective is to find metaheuristics that can, within acceptable computational times, provide sizeable improvements in solution quality over the best existing procedure (a dispatching rule followed by an improvement method).We consider four metaheuristics, namely iterated local search (ILS), iterated greedy (IG), variable greedy (VG) and steady-state genetic algorithms (SSGA). These are known for performing well on permutation flowshops and/or on tardiness criteria. For each metaheuristic, four versions are developed, differing on the choice of initial sequence and/or local search. Additionally, four different time limits are considered. Therefore, a total of 64 sets of results are obtained.The results show that all procedures greatly outperform the best existing method. The IG procedures provide the best results, followed by the SSGA procedures. The VG methods are usually inferior to SSGA, while the ILS metaheuristics tend to be the worst performers.The four metaheuristics prove to be robust in what regards initial solution and local search method, since both have little effect on the performance of the metaheuristics. Increasing the time limit does improve the performance of all procedures. Still, a sizeable improvement is obtained even for the lowest time limit. Therefore, even under restrictive time limits, the metaheuristics greatly outperform the best existing procedure.

Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm

Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state genetic algorithms (GAs). These upper bounds have shown speed-ups of the GAs using crossover and mutation over the same algorithms that only use mutation operators (i.e., steady-state EAs) both for standard unimodal (i.e., OneMax) and multimodal (i.e., Jump) benchmark functions. The bounds suggest that populations are beneficial to the GA as well as higher mutation rates than the default 1/n rate. However, making rigorous claims was not possible because matching lower bounds were not available. Proving lower bounds on crossover-based EAs is a notoriously difficult task as it is hard to capture the progress that a diverse population can make. We use a potential function approach to prove a tight lower bound on the expected runtime of the (2+1) GA for OneMax for all mutation rates c/n with c < 1.422. This provides the last piece of the puzzle that completes the proof that larger population sizes improve the performance of the standard steady-state GA for OneMax for various mutation rates, and it proves that the optimal mutation rate for the (2+1) GA on OneMax is (sqrt{97}-5)/(4n) approx 1.2122/n.

A binary harmony search algorithm as channel selection method for motor imagery-based BCI

BackgroundChannel selection is a key topic in brain-computer interface (BCI). Task-irrelevant and redundant channels used in BCI may lead to low classification accuracy, high computational complexity, and inconvenience for application. By selecting optimal channels, the performance of BCI could enhance significantly. MethodIn this paper, a new binary harmony search (BHS) is proposed to select the optimal channel sets and optimize the system accuracy. The BHS is implemented on the training data sets to select the optimal channels and the test data sets are used to evaluate the classification performance on the selected channels. The sparse representation-based classification, linear discriminant analysis, and support vector machine are performed on the common spatial pattern (CSP) features for motor imagery (MI) classification. ResultsTwo public EEG datasets are employed to validate the proposed BHS method. The paired t-test is conducted on the test classification performance between the BHS and traditional CSP with all channels. The results reveal that the proposed BHS method significantly improved classification accuracy as compared to the conventional CSP method (p < 0.05). ConclusionThis study proposed the BHS method to select the optimal channels in MI -based BCI. On the one hand, the results confirm the validity of the BHS algorithm as a channel selection method for motor imagery data. On the other hand, the BHS method with costing shorter computation time relatively yields a better average test accuracy than the steady-state genetic algorithms. The proposed method could significantly improve the practicability and convenience of the BCI system.

Predicting Software Defects for Object-Oriented Software Using Search-based Techniques

Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.

Guest Editorial Special Issue on Theoretical Foundations of Evolutionary Computation

It is our pleasure to introduce this special issue on the recent advances in the theoretical foundations of evolutionary computation (EC). While in the early days of this field, theoretical analyses inevitably focused on simplified models of evolutionary algorithms (EAs), the continuous progress made in the development of suitable mathematical techniques for the analysis now allows to derive proven statements regarding the performance of off-the-shelf metaheuristics, such as standard generational and steady-state genetic algorithms with no algorithmic simplifications. Comparisons between the performance of a given EA with the best possible one can also be made nowadays, allowing to assess whether a given algorithm may be improved upon or whether its performance is optimal for a given class of problems. Such understanding often provides insights for the design of new EAs which provably have better performance for given problems. We are glad that examples of results of this kind are present within this special issue. A total of 27 papers were submitted which were the subject of at least three independent reviews, and six manuscripts of the highest quality were selected for publication in the special issue. In the following, we provide a brief summary of these manuscripts.

Runtime analysis of evolutionary algorithms via symmetry arguments

We use an elementary argument building on group actions to prove that the selection-free steady state genetic algorithm analyzed by Sutton and Witt (GECCO 2019) takes an expected number of Ω(2n/n) iterations to find any particular target search point. This bound is valid for all population sizes μ. Our result improves over the previous lower bound of Ω(exp⁡(nδ/2)) valid for population sizes μ=O(n1/2−δ), 0<δ<1/2.

On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard (mu +1) GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to mu =oleft( sqrt{log n}right) . Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: (1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. (2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.

An opportunistic preventive maintenance policy for tamping scheduling of railway tracks

ABSTRACT This paper proposes an opportunistic preventive maintenance policy to schedule tamping interventions aiming to minimize the total maintenance cost. In this policy, the goal is to obtain an opportunistic maintenance threshold (OMT) to decide when to perform preventive tamping operations on given railway segments to minimize total maintenance cost associated with tamping operations. A steady-state genetic algorithm was used to simultaneously find an OMT threshold and tamping scheduling. The proposed approach is tested on data collected from a ballasted railway track. The results show that by taking into consideration an OMT threshold, machine preparation cost can be reduced by about 46%. Moreover, by performing a sensitivity analysis of the tamping effectiveness, it is observed that the negative impact of inefficient tamping is greater than the positive impact of efficient tamping on the total maintenance cost and track quality in this case study.

Death and Progress: How Evolvability is Influenced by Intrinsic Mortality.

Many factors influence the evolvability of populations, and this article illustrates how intrinsic mortality (death induced through internal factors) in an evolving population contributes favorably to evolvability on a fixed deceptive fitness landscape. We test for evolvability using the hierarchical if-and-only-if (h-iff) function as a deceptive fitness landscape together with a steady state genetic algorithm (SSGA) with a variable mutation rate and indiscriminate intrinsic mortality rate. The mutation rate and the intrinsic mortality rate display a relationship for finding the global maximum. This relationship was also found when implementing the same deceptive fitness landscape in a spatial model consisting of an evolving population. We also compared the performance of the optimal mutation and mortality rate with a state-of-the-art evolutionary algorithm called age-fitness Pareto optimization (AFPO) and show how the two approaches traverse the h-iff landscape differently. Our results indicate that the intrinsic mortality rate and mutation rate induce random genetic drift that allows a population to efficiently traverse a deceptive fitness landscape. This article gives an overview of how intrinsic mortality influences the evolvability of a population. It thereby supports the premise that programmed death of individuals could have a beneficial effect on the evolvability of the entire population.

Steady-State Sine Cosine Genetic Algorithm Based Chaotic Search for Nonlinear Programming and Engineering Applications

This paper proposes a newly meta-heuristic approach, steady-state sine cosine genetic algorithm-based chaotic search, for solving nonlinear programming and engineering applications. It is a combination of sine cosine approach (SCA), steady-state genetic algorithm (SSGA), and chaotic search (CS), and named as chaos-enhanced SCA with SSGA. The proposed approach integrates SSGA’s exploitation ability and SCA’s exploration ability and local search capability of CS. The performance of the new approach works in two different stages. Firstly, SCA and SSGA start together to increase exploration capability and exploitation tendencies. Secondly, CS used to improve the approximate solution obtained from the first stage and reach the global solution. Hence, the proposed new approach will be more robust as it avoids trapping into local minima in addition to the speed of the search process and rapid convergence towards the global solution. The efficiency of the proposed approach is verified by using it to solve 32 well-known benchmark problems and different engineering design problems. Simulation results show that the proposed approach is competitive and better in most cases as a comparison to others.

Order acceptance and scheduling with sequence-dependent setup times: A new memetic algorithm and benchmark of the state of the art

The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. We propose a novel memetic algorithm for this problem, called Sparrow. It comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to state-of-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.

Optimizing node infiltrations in complex networks by a local search based heuristic

Over the past few years, the task of conceiving effective interventions on complex networks has arisen as different optimization problems. An interesting intervention scheme that has many important real-world applications is to introduce (infiltrate) in a network a certain number of new nodes and connect them to certain existing nodes with the aim of making them as central as possible. The idea is that they should occupy strategic positions in the network to gather a lot of information or to decisively influence others. In this work, we present an optimization problem that concerns the selection of nodes in a network with which to link each of a particular number of infiltrated nodes in order to maximize the lower betweenness centrality value obtained by them. This metric evaluates the participation of the nodes in the communications through the shortest paths of the network and it has been widely used as centrality measure in analyzing social networks. To address the problem, we propose a local search based heuristic whose performance is driven by two search strategies; a constructive greedy procedure that is employed to create an initial solution and a local improvement method that makes use of two neighborhood operators designed for exploring the search space of this problem. It should be noted that the development of metaheuristics for tackling combinatorial problems involving betweenness centrality is very challenging, because this measure is notoriously expensive to compute. That is why we have made two design decisions: firstly, to conceive a very simple metaheuristic approach and secondly, to incorporate in this proposal recent betweenness centrality update techniques that substantially reduce the number of shortest paths which should be re-computed when a network is changed. The performance of our optimizer, with respect to other metaheuristic models from the literature that can be adapted to face this problem, such as a randomized greedy multi-start algorithm and a steady-state genetic algorithm, is empirically shown.

A hybrid genetic algorithm for the degree-constrained minimum spanning tree problem

Given an undirected, connected, edge-weighted graph G and a positive integer d, the degree-constrained minimum spanning tree (dc-MST) problem aims to find a minimum spanning tree T on G subject to the constraint that each vertex is either a leaf vertex or else has degree at most d in T, where d is a given positive integer. The dc-MST is $$\mathcal {NP}$$-hard problem for d$$\ge $$ 2 and finds several real-world applications. This paper proposes a hybrid approach ($$\mathcal {H}$$SSGA) combining a steady-state genetic algorithm and local search strategies for the this problem. An additional step (based on perturbation strategy at a regular interval of time) in the replacement strategy is applied in order to maintain diversity in the population throughout the search process. On a set of available 107 benchmark instances, computational results show the superiority of our proposed $$\mathcal {H}$$SSGA in comparison with the state-of-the-art metaheuristic techniques.

A NEW METHOD FOR SOLVING DEADLOCK USING GENETIC ALGORITHMS

A deadlock is reached when multiple processes share the same resource and prevent each other for accessing the resource. Resource organization is considered the optimal solution to solve this problem. This paper presents a novel approach of steady state genetic algorithms hybrid with banker's algorithm. The sequence of operation starts with features extraction that is used for feeding the Genetic algorithm optimizer. The chromosome structure in this method is the one operation processes correlated with three types of genetic operators: The one-point crossover (1X), two-point crossover (2X), and Uniform crossover (UX). Observe a large number of optimal solutions that avoids the Dead Lock system as a safe state.