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In the modern manufacturing and operations management, on-time delivery is a critical factor towards realizing customer satisfaction. This paper focuses on job-shop scheduling problem to minimize total weighted tardiness and proposes a discrete differential evolution algorithm for this problem. In order to improve the search ability and efficiency, this paper hybrids the local search which is based...
Differential evolution (DE) is a high performance and easy to implement evolutionary algorithm. The DE algorithm with small population size (i.e., micro-DE) can further increase the efficiency of the algorithm. However, it also decreases its exploration capability, causing stagnation and pre-mature convergence. In this paper, the idea of exploration enhancement at the mutation level is proposed. The...
Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial setting for the algorithmic parameters, but these can greatly influence its overall performance. Unlike traditional studies where parameters are tuned...
Web Service Composition (WSC) is a prominent way of actualizing service-oriented architecture by integrating network-accessible Web services into a new invokable application. Evolutionary computation techniques have provided rewarding approaches in automatic Web service composition over the last decade. However, the studies on considering both functionality and non-functionality (i.e. Quality-of-Service,...
In this paper, a new moving block sequence (MBS) representation for resource-constrained project scheduling problems (RCPSPs) is proposed, which is different from the classical activity list that has been widely used for RCPSPs. An activity in a project of RCPSPs has fixed duration and resource demands, thus, it can be modeled as a rectangle block whose height represents the resource demands and width...
Performing data mining tasks on raw time series is inefficient as these data are high-dimensional by nature. Instead, time series are first pre-processed using several techniques before the different data mining tasks can be performed. In general, there are two main approaches to pre-process time series. The first is what we call landmark methods. These methods are based on finding characteristic...
The iterated prisoner's dilemma is a simultaneous two-player game widely used in studies on cooperation and conflict. Past work has shown that the choice of representation or available resources such as the number of states or neurons of evolving agents has a large impact on the behavior of evolved agents. This study revisits three qualities of the agent training algorithm for finite state agents...
A key component of model-based testing is the generation of test data from constraints (e.g., specified in the Object Constraint Language (OCL)) associated with models e.g., specified in the Unified Modeling Language (UML). The quality of test data eventually determines the effectiveness of test cases, e.g., fault detection and coverage. A simple way to generate test data from an OCL constraint is...
Bare-bones Particle Swarm Optimization (BPSO) is a simplified PSO variant, which has shown potential performance on many multimodal optimization problems. However, BPSO is also possible to be trapped into local optima for high-dimensional and complicated optimization problems. In order to enhance the performance of BPSO, this paper presents a modified BPSO, called NMBPSO. It combined the ideas of...
The performance of the Particle Swarm Optimization (PSO) algorithm can be greatly improved if the parameters are appropriately tuned. However, tuning the control parameters for PSO algorithms has traditionally been a time-consuming, empirical process. Furthermore, ideal parameters may be time-dependent. To address the issue of parameter tuning, self-adaptive PSO (SAPSO) algorithms adapt the PSO control...
At present, very little theoretical analysis has been performed on the unified particle swarm optimizer (UPSO). This paper derives the order-1 and order-2 stable regions for the UPSO algorithm, along with the fixed point of particle convergence. The impact that the unification factor has on the stability of UPSO is also analyzed. The theoretical analysis is performed under the stagnation assumption;...
The quantum particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO) algorithm aimed at solving dynamic optimization problems. Some particles in the QPSO algorithm are selected as “quantum” particles and the positions of these particles are sampled, using some probability distribution, within a radius (i.e., a hypersphere) around the global best...
Many real-world optimization problems encounter the presence of uncertainties. Dynamic optimization is a class of problems whose fitness functions vary through time. For these problems, evolutionary algorithm is expected to adapt to the changing environments immediately and find the best solution accurately. Besides, most of the environmental changes may not be too drastic in real-world applications,...
The design of algorithms for Game AI agents usually focuses on the single objective of winning, or maximizing a given score. Even if the heuristic that guides the search (for reinforcement learning or evolutionary approaches) is composed of several factors, these typically provide a single numeric value (reward or fitness, respectively) to be optimized. Multi-Objective approaches are an alternative...
Episode pattern mining is a very powerful technique to get high-valued information for people to solve real-life cross-disciplinary problems, such as for the analysis of manufacturing, stock markets, weather records and so on. As data grows, the mining process must be re-triggered again and again to obtain the most updated information. However, periodically re-mining the full dataset is not cost-effective,...
A general MOPSO algorithm was applied to ZDT1-4. Bias in the archive solutions was observed in the initialisation of the archive solutions. The bias continued until simulation end because a general MOPSO algorithm does not contain any explicit way to correct bias in its archive. Pareto dominance testing was discovered to be a main contributor to the bias. Bias was also introduced by the target's problem...
Protein-ligand docking programs are valuable tools in the modern drug discovery process for predicting the complex structure of a small molecule ligand and the target protein. Often, the configurational search algorithm in the docking tool consists of global search and local search. The former is to explore widely for promising regions in the search space and the latter is to optimize a candidate...
The disaster emergency relief plays a vital role in reducing casualties and economic losses. Emergency logistics scheduling (ELS) aims at dispatching emergency resources to the victims of disasters, which is an important event of disaster relief. In this paper, a model for ELS in disaster relief is built that includes several suppliers with a variety of resources, several kinds of vehicles, and multiple...
The Robust Vehicle Routing Problem with Time Windows has been gaining popularity over the past few years due to its focus on tackling uncertainty inherent to real world problems. Most of the current approaches in generating robust solutions require prior knowledge on the uncertainties, such as uncertainties in travel time. Hence, they are less than favorable to use in the absence of data, i.e., in...
Cognitive radio is a new network technology developed in recent years, which focuses on the low utility of spectrum in the wireless communication system. This paper proposes a new algorithm based on the immune clone optimization for unconstrained multi-objective resource allocation in the downlink OFDMA network. We first convert the constraint of data transmission rate proportionality of each user...
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