Ayad Turky

A multi-population harmony search algorithm with external archive for dynamic optimization problems

Dynamic optimization problems present great challenges to the research community because their parameters are either revealed or changed during the course of an ongoing optimization process. These problems are more challenging than static problems in real-world applications because the latter are usually dynamic, with the environment constantly subjected to change or the size of a problem increasing sporadically. In solving dynamic optimization problems in the real world, proposed solutions should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, a multi-population harmony search algorithm with external archive for dynamic optimization problems is proposed. Harmony search algorithm is a population-based meta-heuristic optimization technique that is similar to a musical process when a musician is attempting to find a state of harmony. To tackle the problem of dynamism, the population of solutions is divided into several sub-populations such that each sub-population takes charge exploring or exploiting the search space. To enhance the algorithm performance further, an external archive is used to save the best solutions for later use. These solutions will then be used to replace redundant solutions in the harmony memory. The proposed algorithm is tested on the Moving Peak Benchmark. Empirical results show that the proposed algorithm produces better results than several of the current state-of-the-art algorithms.

An adaptive multi-population artificial bee colony algorithm for dynamic optimisation problems

Recently, interest in solving real-world problems that change over the time, so called dynamic optimisation problems (DOPs), has grown due to their practical applications. A DOP requires an optimisation algorithm that can dynamically adapt to changes and several methodologies have been integrated with population-based algorithms to address these problems. Multi-population algorithms have been widely used, but it is hard to determine the number of populations to be used for a given problem. This paper proposes an adaptive multi-population artificial bee colony (ABC) algorithm for DOPs. ABC is a simple, yet efficient, nature inspired algorithm for addressing numerical optimisation, which has been successfully used for tackling other optimisation problems. The proposed ABC algorithm has the following features. Firstly it uses multi-populations to cope with dynamic changes, and a clearing scheme to maintain the diversity and enhance the exploration process. Secondly, the number of sub-populations changes over time, to adapt to changes in the search space. The moving peaks benchmark DOP is used to verify the performance of the proposed ABC. Experimental results show that the proposed ABC is superior to the ABC on all tested instances. Compared to state of the art methodologies, our proposed ABC algorithm produces very good results.

A multi-population electromagnetic algorithm for dynamic optimisation problems

This paper is derived from an interest in the development of approaches to tackle dynamic optimisation problems. This is a very challenging research area due to the fact that any approaches utilised should be able to track the changes and simultaneously seek for global optima as the search progresses. In this research work, a multi-population electromagnetic algorithm for dynamic optimisation problems is proposed. An electromagnetic algorithm is a population based meta-heuristic method which imitates the attraction and repulsion of the sample points. In order to track the dynamic changes and to effectively explore the search space, the entire population is divided into several sub-populations (referred as multi-population that acts as diversity mechanisms) where each sub-population takes charge in exploring or exploiting the search space. In addition, further investigation are also conducted on the combination of the electromagnetic algorithm with different diversity mechanisms (i.e. random immigrants, memory mechanism and memory based immigrant schemes) with the aim of identifying the most appropriate diversity mechanism for maintaining the diversity of the population in solving dynamic optimisation problems. The proposed approach has been applied and evaluated against the latest methodologies in reviewed literature of research works with respect to the benchmark problems. This study demonstrates that the electromagnetic algorithm with a multi-population diversity mechanism performs better compared to other population diversity mechanisms investigated in our research and produces some of the best known results when tested on Moving Peak Benchmark (MPB) problems.

A Hybrid Harmony Search Algorithm for Solving Dynamic Optimisation Problems

Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far.

A multi-population memetic algorithm for dynamic shortest path routing in mobile ad-hoc networks

Optimisation under dynamic environment is a well known challenge not only because of the difficulties in handling constant changes during the search progress but also because of its real-world implication as many industry environments are dynamic. To tackle the dynamic aspect, optimisation algorithms need to track the changes and adjust for the global optima simultaneously. In this paper, we propose a multi-population memetic algorithm for dynamic optimisation, specially for the dynamic shortest path routing (DSPR) problem in mobile ad-hoc networks. DSPR is to find the shortest possible path that connects a source node with the destination node under a network environment where the topology is dynamic. There are algorithms proposed for DSPR. However handling the dynamic environment while maintaining the diversity is still a major issue. Hence the multi-population memetic algorithm is designed which has four main parts so the balance between exploration and exploitation of the search space could be better maintained. They include a genetic algorithm part which focuses solely on the exploring the search space; a local search component which is to search around the local area; a multi-population mechanism which is to maintain diversity by allocating every sub-population to different search area; and an external archive which is to preserve the current best solutions. The proposed method has been evaluated on DSPR instances that are generated under both cyclic and acyclic environments. Results show that the proposed algorithm can outperform other methods reported in the literature. That indicates the effectiveness of our proposed multi-population memetic approach in dealing with dynamic optimisation problems.

An Evolutionary Simulating Annealing Algorithm for Google Machine Reassignment Problem

Google Machine Reassignment Problem (GMRP) is a real world problem proposed at ROADEF/EURO challenge 2012 competition which must be solved within 5 min. GMRP consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. This paper proposes an evolutionary simulating annealing (ESA) algorithm for solving this problem. Simulating annealing (SA) is a single solution based heuristic, which has been successfully used in various optimisation problems. The proposed ESA uses a population of solutions instead of a single solution. Each solution has its own SA algorithm and all SAs work in parallel manner. Each SA starts with different initial solution which can lead to a different search path with distinct local optima. In addition, mutation operators are applied once the solution cannot be improved for a certain number of iterations. This will not only help the search avoid being trapped in a local optima, but also reduce computation time. Because new solutions are not generated from scratch but based on existing ones. This study shows that the proposed ESA method can outperform state of the art algorithms on GMRP.

Parallel late acceptance hill-climbing algorithm for the Google machine reassignment problem

Google Machine Reassignment Problem (GMRP) is an optimisation problem proposed at ROADEF/EURO challenge 2012. The task of GMRP is to allocate cloud computing resources by reassigning a set of services to a set of machines while not violating any constraints. We propose an evolutionary parallel late acceptance hill-climbing algorithm (P-LAHC) for GMRP in this study. The aim is to improve the efficiency of search by escaping local optima. Our P-LAHC method involves multiple search processes. It utilises a population of solutions instead of a single solution. Each solution corresponds to one LAHC process. These processes work in parallel to improve the overall search outcome. These LAHC processes start with different initial individuals and follow distinct search paths. That reduces the chance of falling into a same local optima. In addition, mutation operators will apply when the search becomes stagnated for a certain period of time. This further reduces the chance of being trapped by a local optima. Our results on GMRP instances show that the proposed P-LAHC performed better than single threaded LAHC. Furthermore P-LAHC can outperform or at least be comparable to the state-of-the-art methods from the literature, indicating that P-LAHC is an effective search algorithm.

Distributing fibre boards: A practical application of the heterogeneous fleet vehicle routing problem with time windows and three-dimensional loading constraints

Abstract The Heterogeneous Fleet Capacitated Vehicle Routing Problem with Time Windows and Three- Dimensional Loading Constraints (3L-HFCVRPTW) combines the aspects of 3D loading, heterogeneous transport with capacity constraints and time windows for deliveries. It is the first formulation that comprises all these aspects and takes its inspiration from a practical problem of distributing daily fibre board deliveries faced by our industry partner. Given the shape of the goods to transport, the delivery vehicles are customised and their loading constraints take a specialised form. This study introduces the problem and its constraints as well as a specialised procedure for loading the boards. The loading module can be called during or after the route optimisation. In this initial work, we apply simple local search procedures to the routing problem to two data sets obtained from our industry partner and subsequently employ the loading module to place the deliveries on the vehicles. Simulated Annealing outperforms Iterated Local Search, suggesting that the routing problem is multimodal, and operators that shift deliveries between routes appear most beneficial.

A multi-memory multi-population memetic algorithm for dynamic shortest path routing in mobile ad-hoc networks

This study investigates the dynamic shortest path routing (DSPR) problem in mobile ad-hoc networks. The goal is to find the shortest possible path that connects a source node with the destination node while effectively handling dynamic changes occurring on the ad-hoc networks. The key challenge in DSPR is how to simultaneously keep track changes and search for the global optima. A multi-memory based multi-population memetic algorithm is proposed for DSPR in this paper. The proposed algorithm combines the strength of three different strategies, multi-memory, multi-population and memetic algorithm, aiming to effectively explore and exploit the search space. It divides the search space by multiple populations. The distribution of solutions in each population is kept in the associated memory. The multimemory multi-population approach is to capture dynamic changes and maintain search diversity. The memetic component, which is a hybrid Genetic Algorithm (GA) and local search, is to find high quality solutions. The performance of the proposed algorithm is evaluated on benchmark DSPR instances under both cyclic and acyclic environments. Our method obtained better results when compared with existing methods in the literatures, showing the effectiveness of the proposed algorithm in handling dynamic optimisation.

Neighbourhood Analysis: A Case Study on Google Machine Reassignment Problem

It is known that neighbourhood structures affect search performance. In this study we analyse a series of neighbourhood structures to facilitate the search. The well known steepest descent (SD) local search algorithm is used in this study as it is parameter free. The search problem used is the Google Machine Reassignment Problem (GMRP). GMRP is a recent real world problem proposed at ROADEF/EURO challenge 2012 competition. It consists in reassigning a set of services into a set of machines for which the aim is to improve the machine usage while satisfying numerous constraints. In this paper, the effectiveness of three neighbourhood structures and their combinations are evaluated on GMRP instances, which are very diverse in terms of number of processes, resources and machines. The results show that neighbourhood structure does have impact on search performance. A combined neighbourhood structures with SD can achieve results better than SD with single neighbourhood structure.