Search Space Reduction Research Articles

Multi-echelon sustainable reverse logistics network design with incentive mechanism for eco-packages

With the pollution caused by express packaging waste, eco-packages are being commonly used to promote a green supply chain. However, extant research has yet to address the efficacious reverse logistics network design problem while considering both economic and environmental consumption in the recycling process. In this study, a multi-echelon reverse logistics network is designed to collect eco-packages from customers, instead of door-to-door recycling by couriers. This study aims to solve the capacity location-routing problem in a reverse logistics network integrated with the incentive mechanism for customers, which is regarded as NP-hard. To avoid the combinatorial explosion, a multi-objective hybrid genetic algorithm-tabu search (MOHGATS) is developed to reduce search space and improve search speed by changing search strategies. Computational experiments are conducted to verify the proposed model and algorithm based on several test instances and a real-world case study. The impacts of the quantity of eco-packages, the incentive costs for customers, and the quantity and capacity of recycling stations on the reverse logistics network are explored from both economic and environmental perspectives, providing a reference for practical operation and promotion of eco-packages on a large scale.

Reduction of Search Space for the Mean Partition Problem

The contributions of this paper are threefold. First, it conducts a formal comparison of the primary approaches to consensus clustering, using the concepts of agreement and consent. Secondly, it presents theoretical evidence justifying the preference for mean-based methods, which rely on consent, over other agreement-based procedural methods like the q-rule, which are now mostly used as quality evaluators in practice. Thirdly, the paper computes the exact reduction achieved by criteria available in existing literature to assess the quality of mean-based consensus solutions and reduce the search space’s size. Finally, it compiles the regions where consensus functions associated with well-known dissimilarity measures, such as the Mirkin metric and Variation of Information, accumulate their consensus solutions.

A novel competitive constrained dual-archive dual-stage evolutionary algorithm for constrained multiobjective optimization

In constrained multi-objective optimization problems (CMOPs), the key problem is how to balance convergence and diversity simultaneously and find as many feasible solutions as possible in the reduced search space. To address this issue, a novel competitive constrained dual-archive dual-stage evolutionary algorithm, named c-DADSEA, is proposed with a convergence-driven archive (CA) and a diversity-driven archive (DA). To improve the algorithm’s convergence, instead of simply excluding infeasible solutions, an adaptive penalty function is novelly proposed to maintain competitively infeasible solutions in CA. To overcome the difficulty in finding the true Pareto front for the dual-population framework in some problems, a dual-stage framework is introduced to make DA search in different directions at different stages (i.e., Forward and Backward), enhancing the diversity of the algorithm. DA evolves without considering the constraints to explore all regions as much as possible in the Forward stage. While in the Backward stage, DA degenerates with increasing constraints to search the regions around the true Pareto front by using ϵ-constrained method. Moreover, to balance convergence and diversity, different benchmark rankings are designed for the individuals in different archives. The experiments on three benchmark CMOPs test suites demonstrate that c-DADSEA is better than seven other state-of-the-art constrained evolutionary algorithms, especially for the CMOPs with a constrained Pareto front away from the unconstrained Pareto front.

A Search Space Reduction-Based Progressive Evolutionary Algorithm for Influence Maximization in Social Networks

Influence maximization (IM) problem, which selects a subset of nodes from a social network to maximize the influence spread, appeals to numerous scholars. Since the IM problem is NP-hard, it is still an arduous task to achieve good results in terms of influence spread and running time at the same time. This article proposes a novel search space reduction strategy-based progressive evolutionary algorithm (SSR-PEA) for solving IM problems effectively and efficiently. In SSR-PEA, a novel search space reduction strategy (SSR) in the light of the power-law distribution of the social network is designed to reduce the computational overheads, which eliminates a great deal of less influential nodes in a sensible way. After that, we propose a progressive evolutionary framework based on SSR, where the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-element individual is optimized on the basis of the (<inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-1)-element individual to speed up the optimal solution search process. Experimental results on ten real-world networks demonstrate that the proposed algorithm SSR-PEA can achieve 98<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the influence spread achieved by cost-effective lazy forward (CELF) on average, and its running time is two or even three orders of magnitude shorter. Thus, SSR-PEA strikes a better balance between effectiveness and efficiency.

Low-Dimensional Space Modeling-Based Differential Evolution for Large-Scale Global Optimization Problems

Large-Scale Global Optimization (LSGO) has been an active research field. Part of this interest is supported by its application to cutting-edge research such as Deep Learning, Big Data, and complex real-world problems such as image encryption, real-time traffic management, and more. However, the high dimensionality makes solving LSGO a significant challenge. Some recent research deal with the high dimensionality by mapping the optimization process to a reduced alternative space. Nonetheless, these works suffer from the changes in the search space topology and the loss of information caused by the dimensionality reduction. This paper proposes a hybrid metaheuristic, so-called LSMDE (Low-dimensional Space Modeling-based Differential Evolution), that uses the Singular Value Decomposition to build a low-dimensional search space from the features of candidate solutions generated by a new SHADE-based algorithm (GM-SHADE). GM-SHADE combines a Gaussian Mixture Model (GMM) and two specialized local algorithms: MTS-LS1 and L-BFGS-B, to promote a better exploration of the reduced search space. GMM mitigates the loss of information in mapping high-dimensional individuals to low-dimensional individuals. Furthermore, the proposal does not require prior knowledge of the search space topology, which makes it more flexible and adaptable to different LSGO problems. The results indicate that LSMDE is the most efficient method to deal with partially separable functions compared to other state-of-the-art algorithms and has the best overall performance in two of the three proposed experiments. Experimental results also show that the new approach achieves competitive results for non-separable and overlapping functions on the most recent test suite for LSGO problems.

Three-Dimensional Flight Corridor: An Occupancy Checking Process for Unmanned Aerial Vehicle Motion Planning inside Confined Spaces

To deploy Unmanned Aerial Vehicles (UAVs) inside heterogeneous GPS-denied confined (potentially unknown) spaces, such as those encountered in mining and Urban Search and Rescue (USAR), requires the enhancement of numerous technologies. Of special interest is for UAVs to identify collision-freeSafe Flight Corridors (SFC+) within highly cluttered convex- and non-convex-shaped environments, which requires UAVs to perform advanced flight maneuvers while exploiting their flying capabilities. Within this paper, a novel auxiliary occupancy checking process that augments traditional 3D flight corridor generation is proposed. The 3D flight corridor is established as a topological structure based on a hand-crafted path either derived from a computer-generated environment or provided by the human operator, which captures humans’ preferences and desired flight intentions for the given space. This corridor is formulated as a series of interconnected overlapping convex polyhedra bounded by the perceived environmental geometries, which facilitates the generation of suitable 3D flight paths/trajectories that avoid local minima within the corridor boundaries. An occupancy check algorithm is employed to reduce the search space needed to identify 3D obstacle-free spaces in which their constructed polyhedron geometries are replaced with alternate convex polyhedra. To assess the feasibility and efficiency of the proposed SFC+ methodology, a comparative study is conducted against the Star-Convex Method (SCM), a prominent algorithm in the field. The results reveal the superiority of the proposed SFC+ methodology in terms of its computational efficiency and reduced search space for UAV maneuvering solutions. Various challenging confined-environment scenarios, each with different obstacle densities (confined scenarios), are utilized to verify the obtained outcomes.

On the Cryptanalysis of a Simplified AES Using a Hybrid Binary Grey Wolf Optimization

Cryptosystem cryptanalysis is regarded as an NP-Hard task in modern cryptography. Due to block ciphers that are part of a modern cipher and have nonlinearity and low autocorrelation in their structure, traditional techniques and brute-force attacks suffer from breaking the key presented in traditional techniques, and brute-force attacks against modern cipher S-AES (simplified-advanced encryption standard) are complex. Thus, developing robust and reliable optimization with high searching capability is essential. Motivated by this, this paper attempts to present a novel binary hybridization algorithm based on the mathematical procedures of the grey wolf optimizer (GWO) and particle swarm optimization (PSO), named BPSOGWO, to deal with the cryptanalysis of (S-AES). The proposed BPSOGWO employs a known plaintext attack that requires only one pair of plaintext–ciphertext pairs instead of other strategies that require more pairs (i.e., it reduces the number of messages needed in an attack, and secret information such as plaintext-ciphertext pairs cannot be obtained easily). The comprehensive and statistical results indicate that the BPSOGWO is more accurate and provides superior results compared to other peers, where it improved the cryptanalysis accurateness of S-AES by 82.5%, 84.79%, and 79.6% compared to PSO, GA, and ACO, respectively. Furthermore, the proposed BPSOGWO retrieves the optimal key with a significant reduction in search space compared to a brute-force attack. Experiments show that combining the suggested fitness function with HPSOGWO resulted in a 109-fold reduction in the search space. In cryptanalysis, this is a significant factor. The results prove that BPSOGWO is a promising and effective alternative to attack the key employed in the S-AES cipher.

Efficiency enhancement of leakage detection and localization methods using leakage gradient and most affected sensors

Leaks cause substantial economic and water losses for water distribution networks (WDNs). Thus, it is imperative to detect and localize leaks. This paper proposes an efficient set of methods for detecting and localizing leaks, with three main steps: model calibration, leak detection, and leak localization. First, demands, pipe diameter, and pipe roughness in the hydraulic model are calibrated with a genetic algorithm (GA). The X-bar method and cumulative sum control chart are then implemented to detect pipe bursts and incipient leaks, respectively. Lastly, a reduced search space is defined and searched with a GA to locate leaks. The proposed strategy was tested on the hypothetical WDN of L-Town from the Battle of the Leakage Detection and Isolation Methods. Results show that 12 of 19 leaks were detected, and 8 of the 12 leakages were accurately located. Compared with other approaches, the proposed approach is more efficient and equally effective.

Retrofit heat exchanger network optimization via graph-theoretical approach: Pinch-bounded N-best solutions allows positional swapping

Retrofit heat exchanger network (HEN) optimization is a fundamentally unique problem which requires the consideration of existing structures, compared to grassroots design problems. The optimization of retrofit HENs is particularly difficult due to the integration of both existing and newly acquired equipment. The re-routing of existing equipment can lead to various network topologies, increasing the complexity of considerations. In this work, we exploit the P-graph framework to solve retrofit HEN problems, guaranteeing to find the topology of optimal solutions within the constrained space of the HEN retrofit problem. The P-graph framework has additional advantages that allows topologically-efficient search space, simplifies additional unit placement, considers unit positional swapping (re-sequencing and re-piping within search constraints), considers stream splitting, and n-best solution visualization. The pinch minimum utility constraint also provides a bound for the maximum number of modifications in the HEN, significantly reducing search space. The proposed P-graph-based approach is demonstrated using a real refinery case study to show its capability in obtaining the topology of the optimal HEN, highlighting the economic and energy benefits. Further extensions to other retrofit process integration problems (e.g. retrofit water network, hydrogen network etc.) will be enabled via the proposed P-graph approach.

Reinforcement learning approach for multi-agent flexible scheduling problems

Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success in sequential decision-making problems. This research presents a Reinforcement Learning approach for scheduling problems. In particular, this study delivers an OpenAI gym environment with search-space reduction for JSSP and provides a heuristic-guided Q-Learning solution with state-of-the-art performance for Multi-agent Flexible Job Shop Problems.

Water Distribution System Optimization Considering Behind-the-Meter Solar Energy with a Hydraulic Power-Based Search-Space Reduction Method

Water Distribution System Optimization Considering Behind-the-Meter Solar Energy with a Hydraulic Power-Based Search-Space Reduction Method

Scouting Robot with Search-Space Reducing Hybrid Networks for Unknown Environment Path Planning

Autonomous vehicle navigation is becoming an important problem as fully self-driving cars are becoming a possibility in the next few decades and space exploration is gaining more momentum. One of the most important aspects of autonomous vehicle navigation is a solid path planning algorithm. Most path planning algorithms can be categorized into 2 groups, classical and machine learning algorithms. Classical algorithms can find the shortest path and usually have a 100% accuracy, but it can't function when its environment isn’t 100% mapped out. On the other hand, ML algorithms can operate on partial maps or no maps at all, but they have poor success rate and produce long paths. However, a hybrid approach to a path planning algorithm could eliminate the downside of both categories of algorithms. A robot with a hybrid algorithm could have 100% accuracy while maintaining a near optimum route without a map of the environment. This paper proposes a search space reduction hybrid network (SRHN) path planning algorithm that not only combines the advantages of classical methods and machine learning methods, but also reduces the search space and memory usage. SRHN works by dividing up the distance between a start point and an endpoint with landmarks and paths from its current landmark to the next. While calculating an approximate optimal path, it significantly reduces search space. To test the result of SRHN, experimentation was conducted in the real world. Excellent results have been achieved in the real world 2D tests that were conducted.

Bayesian-based optimization of concrete infill pattern for enhancing thermal insulation performance

This study explores the impact of vertical and horizontal configurations on thermal insulation in cellular concrete brick design, aiming to identify optimal insulation patterns. Results indicate that under a constant volume (67%) of coconut fiber, appropriate geometric changes can reduce thermal conductivity by around 10% (from 0.198 W/(m·K) to 0.178 W/(m·K)). Bayesian inference is employed to construct a bi-directional network, providing a more intuitive understanding of variable relationships. A probabilistic-driven search space reduction approach is proposed, improving candidate selection efficiency and reducing the number of assessments. The study introduces a Bayesian Genetic Algorithm (BGA), which outperforms the genetic algorithm when the mutation rate is 0.1.

ManQala: Game-inspired strategies for quantum state engineering

The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here, we recast the fundamental problem of state preparation in high-dimensional Hilbert spaces as ManQala, a quantum game inspired by the West African sowing game mancala. Motivated by optimal gameplay in solitaire mancala, where nested nearest-neighbor permutations and actions evolve the state of the game board to its target configuration, ManQala acts as a pre-processing approach for deterministically arranging particles in a quantum control problem. Once pre-processing with ManQala is complete, existing quantum control methods are applied, but now with a reduced search space. We find that ManQala-type strategies match, or outperform, competing approaches in terms of final state variance even in small-scale quantum state engineering problems where we expect the slightest advantage, since the relative reduction in search space is the least. These results suggest that ManQala provides a rich platform for designing control protocols relevant to quantum technologies.

A power quality‐based framework for optimal siting and sizing of wind turbines by search space reduction via power losses and flicker emission sensitivity indices

AbstractAs a well‐established renewable energy resource, wind energy is being widely used in distribution networks. However, flicker emission produced by wind turbines (WTs) is one of the main disadvantages which may cause serious problems during the system operation. Power quality‐based optimal allocation (siting and sizing) of WTs is an appropriate way to mitigate the flicker emission. Since the planning issue is a high dimensional and complex optimization problem and optimization methods suffer from premature convergence, search space reduction may be an effective approach to optimally solve the allocation problem. Here, to determine the importance of the network buses, two indices, namely, power loss index (PLI) and flicker emission index (FEI) are introduced to determine the sensitivity of the network losses and flicker emission to allocate WT on each bus. Then, based on the proposed indices, a number of important buses are identified and the allocation problem is solved with respect to these buses. Over the case study, it can be drawn that optimal allocation of WTs considerably affects the flicker emission. Moreover, search space reduction is an efficient way to optimally solve the allocation problem.

Simplified shear wall building model for design optimization

Structural design can be inefficient due to its manual and iterative nature, requiring costly and time-consuming revisions by experienced engineers; thus, there is a growing demand for automated and intelligent design approaches. In light of these factors, we propose a simplified model of shear wall buildings based on connected 3D beam columns to quickly derive the fundamental period, base shear, and moment. Our approach formulation considers an initial topologic configuration of the shear wall rectangular members, featuring intricate element connectivity and variable layout for the basement, first, and typical floors, offering a flexible approach to handle inputs from various sources, like grid-based layouts or vectorized image floor plans. Concerning the model, the column-based design yields an adequate estimation of the structure's fundamental translational period, with an R2 of around 0.7 and a roof displacement with an R2 of 0.85. Our findings, coupled with an average computing time of 1.6 s, enable the model integration into machine learning pipelines or the swift optimization of solutions by assembling fitness functions that consider parameters like building weight, roof displacement, or base shear and moment, ultimately reducing search space and saving costs in the design process.

Search Space Reduction for Localization and Tracking of an Acoustic Source

Experimental data from the SACLANTCEN 1993 Mediterranean Experiment are reviewed to assess the reduction of the search space for the localization and tracking of an acoustic source in a three-dimensional environment. Key to this goal is the availability of an initial estimate of source range and depth (called the 2D initial guess); an ambiguous estimate of source bearing can be obtained from the 2D initial guess through Environmental Signal Processing, and the ambiguity can be removed by searching for the source only in the range/bearing regions where bearing estimates are higher. This search provides a new estimate of source range and a single bearing, which together with the estimate for source depth constitute the center of the reduced search space for source localization and tracking. The suggested approach is tested on experimental data from the SACLANTCEN experiment considering different frequencies, as well as a stationary and a moving source.

An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing

This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks.

Techno-economic analysis of grid-connected hybrid renewable energy system adapting hybrid demand response program and novel energy management strategy

Over the last two decades, there has been a significant interest in developing solutions to improve the efficiency of power systems, such as optimal energy consumption and management. The demand response programs (DRP) have long been one of the most effective strategies to encourage consumers to change their consumption behavior. In this paper, a novel hybrid DRP has been implemented by combining the time of use (TOU) and incentive-based (IB) DRP methods. To meet the load demand, a PV-WT-BESS hybrid renewable energy system (HRES) has been proposed in this work. Further, to analyze the impact of hybrid DRP on the capacity allocation of different system components, optimal sizing has been performed in two scenarios, one with the actual load and the second with the load after considering the hybrid DRP. The optimization has been carried out by using an improved search space reduction (ISSR) algorithm. To demonstrate the effectiveness of the ISSR algorithm, the obtained results are compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning based algorithm (TLBO), seagull optimization algorithm (SOA), and sine cosine algorithm (SCA). Finally, a novel energy management strategy (EMS) is also proposed in this work on the supply side of the HRES, which intelligently uses the variations in the grid tariff to charge the BESS when the tariff is low. To demonstrate the proposed DRP and EMS-based sizing, a case study has been performed for a grid-connected PV-WT-BESS for the location of Kanyakumari, India. From the results obtained, it has been concluded that the hybrid DRP reduces the peak demand up to 5%, which reduces the levelized cost of energy (LCE) by 22%. Furthermore, the proposed novel EMS reduces the LCE further by 5%. The overall cost reduction of up to 25% has been achieved by using both the proposed hybrid DRP and EMS, while satisfying all necessary constraints.

The Use of Graph Theory for Search Space Reduction in Contaminant Source Identification

Contaminant intrusion in water distribution networks is an important concern and must be identified as soon as possible. Although optimization techniques are widely used for source identification, the performance of these methods decreases with increasing the size of the water network. To overcome this problem, this paper presents a new approach to reduce the search space, based on network topology, as a prior step to applying a search algorithm. In this method, all hydraulically connected nodes to the sensor node (with a positive detection) are identified using graph theory and considered as the new reduced search space in the optimization process. The effectiveness of the proposed method was investigated on three different benchmark water networks with different complexity and sizes. The results showed that the proposed approach can decrease the computational time by 18% to 63% while improving the success rate of contaminant source identification by 14% to 150%.