The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and i...
The Solution of the Subproblem of the Cutting Angle Method of Global Optimization for problems of minimizing increasing Positively Homogeneous of degree one functions is proved to be NP-Complete. For the proof of this fact we formulate another proble...
In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. Howev...
Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field...
In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search spa...
This work introduces a fuzzy optimization model, which solves in an integrated way the berth allocation problem (BAP) and the quay crane allocation problem (QCAP). The problem is solved for multiple quays, considering vessels’ imprecise arrival times...
Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not v...
The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization pr...
The Set Coverage Problem (SCP) is an important combinatorial optimization problem known to be NP-complete. The use of metaheuristics to solve the SCP includes different algorithms. In particular, binarization techniques have been explored to adapt me...
A pickup and delivery problem by multiple agents has many applications, such as food delivery service and disaster rescue. In this problem, there are cases where fuels must be considered (e.g., the case of using drones as agents). In addition, there...
This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm’s decoder first defines the number of...
The manta ray foraging optimization algorithm (MRFO) is one of the promised meta-heuristic optimization algorithms. However, it can stick to a local minimum, consuming iterations without reaching the optimum solution. So, this paper proposes a hybrid...
The main purpose of the work was examining various methods of decomposition of a network optimization problem of simultaneous routing and bandwidth allocation based on Lagrangian relaxation. The problem studied is an NP-hard mixed-integer nonlinear o...
The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health...
Gear train optimization problems (GTOPs) can be very difficult. This paper proposes an enumerative optimization procedure (EOP) for the GTOP of a two-speed dedicated electric transmission (2DET) for electric vehicles (EVs). The EOP combines enumerati...
Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, na...
This article addresses the problem of converting a single-objective combinatorial problem into a multi-objective one using the Pareto front approach. Although existing algorithms can identify the optimal solution in a multi-objective space, they fail...
The counterpart for Euler’s buckling problem is Greenhill’s problem, which studies the forming of a loop in an elastic beam under torsion. In the context of twisted shafts, the optimal shape of the beam along its axis is searched. A prior...
The ant colony algorithm faces dimensional catastrophe problems when solving the large-scale traveling salesman problem, which leads to unsatisfactory solution quality and convergence speed. To solve this problem, an adaptive ant colony optimization...
In the wake of quantum computing advancements and quantum algorithmic progress, quantum algorithms are increasingly being employed to address a myriad of combinatorial optimization problems. Among these, the Independent Domination Problem (IDP), a de...
The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intra...
The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, t...
The vehicle-routing problem (VRP) is a popular area of research. This popularity springs from its wide application in many real-world problems, such as logistics, network routing, E-commerce, and various other fields. The VRP is simple to formulate,...
The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a mo...
The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists o...
In this paper, we consider the symmetric matrix optimization problem arising in the process of unsupervised feature selection. By relaxing the orthogonal constraint, this problem is transformed into a constrained symmetric nonnegative matrix optimiza...
Modern computational mathematics and informatics for Digital Environments deal with the high dimensionality when designing and optimizing models for various real-world phenomena. Large-scale global black-box optimization (LSGO) is still a hard proble...
Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the con...
The fuzzy transportation problem (FTP) represents a significant extension of the classical transportation problem (TP) by introducing uncertainly and imprecision into the parameters involved. Various algorithms have been proposed to solve the FTP, in...
In the era of open data and open science, it is important that, before announcing their new results, authors consider all previous studies and ensure that they have competitive material worth publishing. To save time, it is popular to replace the exh...
The preservation and maintenance of ancient buildings, particularly heritage buildings, is a complex process that requires careful consideration of several aspects, including maintenance plans and budget availability. An effective long-term maintenan...
This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicle...
The Cell Formation Problem plays a fundamental role in cellular manufacturing due to its impact on efficiency, flexibility, and reliability. Its complexity increases under real-world conditions involving alternative process routes and machine reliabi...
In this paper we introduce the draft of a new graph-based algorithm for optimization of scheduling problems. Our algorithm is based on the Generalized Lifelong Planning A* algorithm, which is usually used for path planning for mobile robots. It was t...
Ant colony optimization (ACO) has proven its adaptation capabilities on optimization problems with dynamic environments. In this work, the dynamic traveling salesman problem (DTSP) is used as the base problem to generate dynamic test cases. Two types...
This paper introduces a topology optimization framework for steady incompressible Stokes flow based on the non-conforming Virtual Element Method, VEM. The proposed framework combines the geometric flexibility of VEM with an optimality criteria update...
In this work, we investigate the variant of the Internet Shopping Optimization Problem (ISHOP) that considers different item units. This variant is more challenging than the original problem. The original ISHOP is already known as a combinatorial NP-...
This study aims to minimize the makespan objective function in a Flow-Shop environment, considering two crucial temporal constraints: “Waiting time” and “Release date”. Given the NP-hardness of this scheduling problem, we empl...
A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with impreci...
The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibili...
The two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to...
The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previou...
This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to so...
This study introduces a novel heuristic approach to optimize mineral processing in metallurgical plants, framed as a variant of the fractional knapsack problem. The optimization framework integrates plant operational modes, blending requirements, and...
The Transportation Problem (TP) is a special type of linear programming problem, where the objective is to minimize the cost of distributing a product from a number of sources to a number of destinations. Many methods for solving the TP have been stu...
The Capacitated Quadratic Assignment Problem (CQAP) arises in logistics and network design, requiring the allocation of tasks to agents under quadratic interaction costs and capacity constraints. Classical exact solvers become computationally infeasi...
This paper addresses the problem of optimizing the voltage profile of radially-operated distribution systems by acting on the active and reactive powers provided by distributed energy resources (DERs). A novel voltage optimization procedure is propos...
The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem with broad applications in logistics and smart agriculture. However, despite significant progress in Transformer-based deep reinforcement learning methods, two major...
In this paper, we delve into the realm of biconvex optimization problems, introducing an adaptive Douglas–Rachford algorithm and presenting related convergence theorems in the setting of finite-dimensional real Hilbert spaces. It is worth notin...
The Static Economic Load Dispatch (SELD) problem is a paramount optimization challenge in power engineering that seeks to optimize the allocation of power between generating units to meet imposed constraints while minimizing energy requirements. Rece...
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