Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Autonomous Supply Chai

A Hybrid Proposed Imperialist Competitive Algorithm with Conjugate Gradient Approach for Large Scale Global Optimization

This paper presents a novel hybrid imperialist competitive algorithm called ICA-CG algorithm. Such an algorithm combines the evolution ideas of the imperialist competitive algorithm and the classic optimization ideas of the conjugate gradient, based on the compensation for solving the large scale optimization. In the ICA-CG algorithm, the process of every iteration is divided into two stages. In the first stage, the randomly, rapidity and wholeness of the imperialist competitive Algorithm are used. In the second stage, one of the common optimization classical techniques, that called conjugate gradient to move imperialist countries, is used. Experimental results for five well known test problems have shown the superiority of the new ICA-CG algorithm, in large scale optimization, compared with the classical GA, ICA, PSO and ABC algorithms, with regard to the convergence of speed and quality of obtained solutions

Solving no-wait two-stage flexible flow shop scheduling problem with unrelated parallel machines and rework time by the adjusted discrete Multi Objective Invasive Weed Optimization and fuzzy dominance approach

Purpose: Adjusted discrete Multi-Objective Invasive Weed Optimization (DMOIWO) algorithm, which uses fuzzy dominant approach for ordering, has been proposed to solve No-wait two-stage flexible flow shop scheduling problem. Design/methodology/approach: No-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times and probable rework in both stations, different ready times for all jobs and rework times for both stations as well as unrelated parallel machines with regards to the simultaneous minimization of maximum job completion time and average latency functions have been investigated in a multi-objective manner. In this study, the parameter setting has been carried out using Taguchi Method based on the quality indicator for beater performance of the algorithm. Findings: The results of this algorithm have been compared with those of conventional, multi-objective algorithms to show the better performance of the proposed algorithm. The results clearly indicated the greater performance of the proposed algorithm. Originality/value: This study provides an efficient method for solving multi objective no-wait two-stage flexible flow shop scheduling problem by considering sequence-dependent setup times, probable rework in both stations, different ready times for all jobs, rework times for both stations and unrelated parallel machines which are the real constraints.Peer Reviewe

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective scheduling of parallel-unrelated machines where setup times are sequence dependent. The objectives include mean completion tasks and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better

An imperialist competitive algorithm for a bi-objective parallel machine scheduling problem with load balancing consideration

In this paper, we present a new Imperialist Competitive Algorithm (ICA) to solve a bi-objective unrelated parallel machine scheduling problem where setup times are sequence dependent. The objectives include mean completion time of jobs and mean squares of deviations from machines workload from their averages. The performance of the proposed ICA (PICA) method is examined using some randomly generated data and they are compared with three alternative methods including particle swarm optimization (PSO), original version of imperialist competitive algorithm (OICA) and genetic algorithm (GA) in terms of the objective function values. The preliminary results indicate that the proposed study outperforms other alternative methods. In addition, while OICA performs the worst as alternative solution strategy, PSO and GA seem to perform better

A general Framework for Utilizing Metaheuristic Optimization for Sustainable Unrelated Parallel Machine Scheduling: A concise overview

Sustainable development has emerged as a global priority, and industries are increasingly striving to align their operations with sustainable practices. Parallel machine scheduling (PMS) is a critical aspect of production planning that directly impacts resource utilization and operational efficiency. In this paper, we investigate the application of metaheuristic optimization algorithms to address the unrelated parallel machine scheduling problem (UPMSP) through the lens of sustainable development goals (SDGs). The primary objective of this study is to explore how metaheuristic optimization algorithms can contribute to achieving sustainable development goals in the context of UPMSP. We examine a range of metaheuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and more, and assess their effectiveness in optimizing the scheduling problem. The algorithms are evaluated based on their ability to improve resource utilization, minimize energy consumption, reduce environmental impact, and promote socially responsible production practices. To conduct a comprehensive analysis, we consider UPMSP instances that incorporate sustainability-related constraints and objectives

Distributed and Lightweight Meta-heuristic Optimization method for Complex Problems

The world is becoming more prominent and more complex every day. The resources are limited and efficiently use them is one of the most requirement. Finding an Efficient and optimal solution in complex problems needs to practical methods. During the last decades, several optimization approaches have been presented that they can apply to different optimization problems, and they can achieve different performance on various problems. Different parameters can have a significant effect on the results, such as the type of search spaces. Between the main categories of optimization methods (deterministic and stochastic methods), stochastic optimization methods work more efficient on big complex problems than deterministic methods. But in highly complex problems, stochastic optimization methods also have some issues, such as execution time, convergence to local optimum, incompatible with distributed systems, and dependence on the type of search spaces. Therefore this thesis presents a distributed and lightweight metaheuristic optimization method (MICGA) for complex problems focusing on four main tracks. 1) The primary goal is to improve the execution time by MICGA. 2) The proposed method increases the stability and reliability of the results by using the multi-population strategy in the second track. 3) MICGA is compatible with distributed systems. 4) Finally, MICGA is applied to the different type of optimization problems with other kinds of search spaces (continuous, discrete and order based optimization problems). MICGA has been compared with other efficient optimization approaches. The results show the proposed work has been achieved enough improvement on the main issues of the stochastic methods that are mentioned before.Maailmasta on päivä päivältä tulossa yhä monimutkaisempi. Resurssit ovat rajalliset, ja siksi niiden tehokas käyttö on erittäin tärkeää. Tehokkaan ja optimaalisen ratkaisun löytäminen monimutkaisiin ongelmiin vaatii tehokkaita käytännön menetelmiä. Viime vuosikymmenien aikana on ehdotettu useita optimointimenetelmiä, joilla jokaisella on vahvuutensa ja heikkoutensa suorituskyvyn ja tarkkuuden suhteen erityyppisten ongelmien ratkaisemisessa. Parametreilla, kuten hakuavaruuden tyypillä, voi olla merkittävä vaikutus tuloksiin. Optimointimenetelmien pääryhmistä (deterministiset ja stokastiset menetelmät) stokastinen optimointi toimii suurissa monimutkaisissa ongelmissa tehokkaammin kuin deterministinen optimointi. Erittäin monimutkaisissa ongelmissa stokastisilla optimointimenetelmillä on kuitenkin myös joitain ongelmia, kuten korkeat suoritusajat, päätyminen paikallisiin optimipisteisiin, yhteensopimattomuus hajautetun toteutuksen kanssa ja riippuvuus hakuavaruuden tyypistä. Tämä opinnäytetyö esittelee hajautetun ja kevyen metaheuristisen optimointimenetelmän (MICGA) monimutkaisille ongelmille keskittyen neljään päätavoitteeseen: 1) Ensisijaisena tavoitteena on pienentää suoritusaikaa MICGA:n avulla. 2) Lisäksi ehdotettu menetelmä lisää tulosten vakautta ja luotettavuutta käyttämällä monipopulaatiostrategiaa. 3) MICGA tukee hajautettua toteutusta. 4) Lopuksi MICGA-menetelmää sovelletaan erilaisiin optimointiongelmiin, jotka edustavat erityyppisiä hakuavaruuksia (jatkuvat, diskreetit ja järjestykseen perustuvat optimointiongelmat). Työssä MICGA-menetelmää verrataan muihin tehokkaisiin optimointimenetelmiin. Tulokset osoittavat, että ehdotetulla menetelmällä saavutetaan selkeitä parannuksia yllä mainittuihin stokastisten menetelmien pääongelmiin liittyen

OptPlatform: metaheuristic optimisation framework for solving complex real-world problems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWe optimise daily, whether that is planning a round trip that visits the most attractions within a given holiday budget or just taking a train instead of driving a car in a rush hour. Many problems, just like these, are solved by individuals as part of our daily schedule, and they are effortless and straightforward. If we now scale that to many individuals with many different schedules, like a school timetable, we get to a point where it is just not feasible or practical to solve by hand. In such instances, optimisation methods are used to obtain an optimal solution. In this thesis, a practical approach to optimisation has been taken by developing an optimisation platform with all the necessary tools to be used by practitioners who are not necessarily familiar with the subject of optimisation. First, a high-performance metaheuristic optimisation framework (MOF) called OptPlatform is implemented, and the versatility and performance are evaluated across multiple benchmarks and real-world optimisation problems. Results show that, compared to competing MOFs, the OptPlatform outperforms in both the solution quality and computation time. Second, the most suitable hardware platform for OptPlatform is determined by an in-depth analysis of Ant Colony Optimisation scaling across CPU, GPU and enterprise Xeon Phi. Contrary to the common benchmark problems used in the literature, the supply chain problem solved could not scale on GPUs. Third, a variety of metaheuristics are implemented into OptPlatform. Including, a new metaheuristic based on Imperialist Competitive Algorithm (ICA), called ICA with Independence and Constrained Assimilation (ICAwICA) is proposed. The ICAwICA was compared against two different types of benchmark problems, and results show the versatile application of the algorithm, matching and in some cases outperforming the custom-tuned approaches. Finally, essential MOF features like automatic algorithm selection and tuning, lacking on existing frameworks, are implemented in OptPlatform. Two novel approaches are proposed and compared to existing methods. Results indicate the superiority of the implemented tuning algorithms within constrained tuning budget environment

Rock mass classification for predicting environmental impact of blasting on tropically weathered rock

Tropical climate and post tectonic impact on the rock mass cause severe and deep weathering in complex rock formations. The uniqueness of tropical influence on the geoengineering properties of rock mass leads to significant effects on blast performance especially in the developmental stage. Different rock types such as limestone and granite exhibit different weathering effects which require special attention for classifying rock mass for blastability purpose. Rock mass classification systems have been implemented for last century for various applications to simplify complexity of rock mass. Several research studies have been carried out on rock mass and material properties for five classes of weathered rock- fresh, slightly, moderately, highly and completely weathered rock. There is wide variation in rock mass properties- heterogeneity and strength of weathered rocks in different weathering zones which cause environmental effects due to blasting. Several researchers have developed different techniques for prediction of air overpressure (AOp), peak particle velocity (PPV) and flyrock primarily for production blast. These techniques may not be suitable for prediction of blast performance in development benches in tropically weathered rock mass. In this research, blast monitoring program were carried out from a limestone quarry and two granite quarries. Due to different nature of properties, tropically weathered rock mass was classified as massive, blocky and fractured rock for simpler evaluation of development blast performance. Weathering Index (WI) is introduced based on porosity, water absorption and Point Load Index (PLI) strength properties of rock. Weathering index, porosity index, water absorption index and point load index ratio showed decreasing trend from massive to fractured tropically weathered rock. On the other hand, Block Weathering Index (BWI) was developed based on hypothetical values of exploration data and computational model. Ten blasting data sets were collected for analysis with blasting data varying from 105 to 166 per data set for AOp, PPV and flyrock. For granite, one data set each was analyzed for AOp and PPV and balance five data sets were analyzed for flyrock in granite by variation in input parameters. For prediction of blasting performance, varied techniques such as empirical equations, multivariable regression analysis (MVRA), hypothetical model, computational techniques (artificial intelligence-AI, machine learning- ML) and graphical charts. Measured values of blast performance was also compared with prediction techniques used by previous researchers. Blastability Index (BI), powder factor, WI are found suitable for prediction of all blast performance. Maximum charge per delay, distance of monitoring point are found to be critical factors for prediction of AOp and PPV. Stiffness ratio is found to be a crucial factor for flyrock especially during developmental blast. Empirical equations developed for prediction of PPV in fractured, blocky, and massive limestone showed R2 (0.82, 0.54, and 0.23) respectively confirming that there is an impact of weathering on blasting performance. Best fit equation was developed with multivariable regression analysis (MVRA) with measured blast performance values and input parameters. Prediction of flyrock for granite with MVRA for massive, blocky and fractured demonstrated R2 (0.8843, 0.86, 0.9782) respectively. WI and BWI were interchangeably used and results showed comparable results. For limestone, AOp analysed with model PSO-ANN showed R2(0.961); PPV evaluated with model FA-ANN produced R2 (0.966). For flyrock in granite with prediction model GWO-ANFIS showed R2 (1) The same data set was analysed by replacing WI with BWI showed equivalent results. Model ANFIS produced R2 (1). It is found the best performing models were PSO-ANN for AOp, FA-ANN for PPV and GWO-ANFIS for flyrock. Prediction charts were developed for AOp, PPV and flyrock for simple in use by site personnel. Blastability index and weathering index showed variation with reclassified weathering zones – massive, blocky and fractured and they are useful input parameters for prediction of blast performance in tropically weathered rock

Artificial Intelligence and International Conflict in Cyberspace

This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation