The increasing complexity and variety of IoT systems require the integration of multiple services to meet a wide range of user needs. This paper addresses the challenge of multi-objective IoT service composition with replication problem by considering multiple Quality of Service (QoS) metrics such as response time and the number of selected service instances. We propose a new Memetic NSGA-II algorithm with Bottleneck-driven Local Search (MNSGA2-BLS) to effectively solve this difficult problem. By integrating genetic operations, clustering-based refinement, and a bottleneck-driven Estimation of Distribution Algorithm for local search, MNSGA2-BLS identifies and optimizes critical service instances causing QoS bottlenecks. This method leverages Pareto-optimal solutions to guide the local search refinement process, enhancing convergence and solution quality. Experimental results across various benchmark cases demonstrate that MNSGA2-BLS can outperform NSGA-II and several state-of-the-art algorithms, achieving superior results in both the hyper-volume and inverse generational distance metrics. This highlights the potential of MNSGA2-BLS to provide efficient and effective composite IoT services while addressing trade-offs between competing QoS objectives.

As optimization algorithms progress to address more complex and high-dimensional challenges, the need for benchmark problems that are both diverse and controllable has become crucial for effective performance evaluation. However, traditional benchmark problem generators often fall short in capturing the required diversity and controllability, limiting their effectiveness in assessing algorithm performance. This paper introduces a novel Controllable Chaotic Landscape Generator (CCLG), designed to enhance the controllability of generated landscapes through the integration of optimization techniques, while maintaining high diversity. This study leverages common problem attributes from the BBOB benchmark suite as targets, enabling effective control over both local and global characteristics of the generated problems, such as the positions of local optima, condition numbers, ruggedness, and global structure. Experimental results demonstrate that CCLG not only achieves effective control over landscape features but also preserves high diversity to meet various optimization requirements.

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially Estimation of Distribution Algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles. In this study, we propose a generative adversarial net-based broad-spectrum global optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with strategies to balance exploration-exploitation trade-off. It has potential to better adapt to the regularity and structure of diversified landscapes than other methods with fixed prior, e.g., Gaussian assumption or separability. Experiments on diverse BBO benchmarks and high dimensional real world applications exhibit that OPT-GAN outperforms other traditional and neural net-based BBO algorithms. The code and Appendix are available at https://github.com/NBICLAB/OPT-GAN

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem.

Particle swarm optimization (PSO) in recent years has been widely applied to solve various real world problems. However, for ill conditioned problems with largely different sensitivity to the objective function, classical PSO cannot search for optimal solution efficiently due to the best position-guided strategy that wastes lots of source searching undesirable areas. Therefore, this paper proposes a novel velocity reinforced mechanism (VR) for solving m-conditional problems. Two implementations of the mechanism, velocity reinforced particle swarm optimization and velocity reinforced search, are introduced in this paper. VR updates its velocity by learning and correcting best velocity directly, instead of using classical best position-guided updating rules. In this way, it increases the possibility that finds better directions for m-conditional problems. Experiments indicate that the novel approaches improve the final results and efficiency.

Classical benchmark problems utilize multiple transformation techniques to increase optimization difficulty, e.g., shift for anti centering effect and rotation for anti dimension sensitivity. Despite testing the transformation invariance, however, such operations do not really change the landscape's "shape", but rather than change the "view point". For instance, after rotated, ill conditional problems are turned around in terms of orientation but still keep proportional components, which, to some extent, does not create much obstacle in optimization. In this paper, inspired from image processing, we investigate a novel graphic conformal mapping transformation on benchmark problems to deform the function shape. The bending operation does not alter the function basic properties, e.g., a unimodal function can almost maintain its unimodality after bent, but can modify the shape of interested area in the search space. Experiments indicate the same optimizer spends more search budget and encounter more failures on the conformal bent functions than the rotated version. Several parameters of the proposed function are also analyzed to reveal performance sensitivity of the evolutionary algorithms.