Solving “Limited” Task Allocation Problem for UAVs Based on Optimization Algorithms

With the rapid development of science and technology, unmanned technology has been widely used in many fields. One of the most important applications is in the field of civil and military UAVs. In the field of military UAVs (unmanned aerial vehicles), UAVs usually have to complete a series of tasks. In this series of tasks, there are often some key tasks. Key tasks play an important role, which is highly related to the feasibility of the whole action or task; mission failure sometimes causes incalculable damage. When assigning tasks to UAVs, it is necessary to ensure the accurate implementation of key tasks, so as to ensure the orderly implementation of the overall task. This paper not only successfully solved the previous problems but also comprehensively considered the minimization of resource consumption and the maximization of task revenue in the process of UAV task allocation. On the basis of considering the key system, considering the constraints and multiobjective problems in the UAV task allocation process, the violence allocation algorithm, constraint optimization evolutionary algorithm, PSO algorithm, and greedy algorithm combined with a constraint evolutionary algorithm are improved and optimized; it has been proven that they can solve the above difficulties. At the same time, several comparison experiments have been carried out; the performance and conclusion of the above four algorithms in the “limited” UAV task allocation scheme are analyzed in the experimental part.

An enhanced auto adaptive vector evaluated-based metaheuristic for solving real-world problems

This article investigates the enhancement of a vector evaluat-ed-based adaptive metaheuristics for solving two multiobjective problems called environmental-economic dispatch and portfolio optimization. The idea is to evolve two populations independently, and exchange information between them, i.e., the first population evolves according to the best individual of the second population and vice-versa. The choice of which algorithm will be executed on each generation is carried out stochastically among three evolutionary algorithms well-known in the literature: PSO, DE, ABC. To assess the results, we used an established metric in multiobjective evolutionary algorithms called hypervolume. Tests solving the referred problem have shown that the new approach reaches the best hypervolumes in power systems comprised of six and forty generators and five different datasets of portfolio optimization. The experiments were performed 31 times, using 250, 500, and 1000 iterations in both problems. Results have also shown that our proposal tends to overcome a variation of a hybrid SPEA2 compared to their cooperative and competitive approaches.

Improved Membrane Algorithm Under the Framework of P Systems to Solve Multimodal Multiobjective Problems

Multimodal multiobjective problems (MMOPs) exist in scientific research and practical projects, and their Pareto solution sets correspond to the same Pareto front. Existing evolutionary algorithms often fall into local optima when solving such problems, which usually leads to insufficient search solutions and their uneven distribution in the Pareto front. In this work, an improved membrane algorithm is proposed for solving MMOPs, which is based on the framework of P system. More specifically, the proposed algorithm employs three elements from P system: object, reaction rule, and membrane structure. The object is implemented by real number coding and represents a candidate solution to the optimization problem to be solved. The function of the reaction rule of the proposed algorithm is similar to the evolution operation of the evolutionary algorithm. It can evolve the object to obtain a better candidate solution set. The membrane structure is the evolutionary logic of the proposed algorithm. It consists of several membranes, each of which is an independent evolutionary unit. This structure is used to maintain the diversity of objects, so that it provides multiple Pareto sets as output. The effectiveness verification study was carried out in simulation experiments. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving all 22 multimodal benchmark test problems in CEC2019.

Approximation Methods for Multiobjective Optimization Problems: A Survey

Algorithms for approximating the nondominated set of multiobjective optimization problems are reviewed. The approaches are categorized into general methods that are applicable under mild assumptions and, thus, to a wide range of problems, and into algorithms that are specifically tailored to structured problems. All in all, this survey covers 52 articles published within the last 41 years, that is, between 1979 and 2020. Summary of Contribution: In many problems in operations research, several conflicting objective functions have to be optimized simultaneously, and one is interested in finding Pareto optimal solutions. Because of the high complexity of finding Pareto optimal solutions and their usually very large number, however, the exact solution of such multiobjective problems is often very difficult, which motivates the study of approximation algorithms for multiobjective optimization problems. This research area uses techniques and methods from algorithmics and computing in order to efficiently determine approximate solutions to many well-known multiobjective problems from operations research. Even though approximation algorithms for multiobjective optimization problems have been investigated for more than 40 years and more than 50 research articles have been published on this topic, this paper provides the first survey of this important area at the intersection of computing and operations research.

Sectorization for Water Distribution Systems with Multiple Sources: A Performance Indices Comparison

Sectorization is an effective technique for reducing the complexities of analyzing and managing of water systems. The resulting sectors, called district metering areas (DMAs), are expected to meet some requirements and performance criteria such as minimum number of intervention, pressure uniformity, similarity of demands, water quality and number of districts. An efficient methodology to achieve all these requirements together and the proper choice of a criteria governing the sectorization is one of the open questions about optimal DMAs design. This question is addressed in this research by highlighting the advantages of three different criteria when applied to real-word water distribution networks (WDNs). To this, here it is presented a two-stage approach for optimal design of DMAs. The first stage, the clustering of the system, is based on a Louvain-type greedy algorithm for the generalized modularity maximization. The second stage, the physical dividing of the system, is stated as a two-objective optimization problem that utilises the SMOSA version of simulated annealing for multiobjective problems. One objective is the number of isolation valves whereas for the second objective three different performance indices (PIs) are analyzed and compared: (a) standard deviation, (b) Gini coefficient and (c) loss of resilience. The methodology is applied to two real case studies where the first two PIs are optimized to address similar demands among DMAs. The results demonstrate that the proposed method is effective for sectorization into independent DMAs with similar demands. Surprisingly, it found that for the real studied systems, loss of resilience achieves better performance for each district in terms of pressure uniformity and demand similarity than the other two specific performance criteria.

Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to a “numerical” population of solutions to optimize a fitness function. GA implementations must find a compromise between the breath of the search (to avoid being trapped into local minima) and its depth (to prevent a rough approximation of the optimal solution). Most algorithms use “elitism”, which allows preserving some of the current best solutions in the successive generations. If the initial population is randomly selected, as in many GA packages, the elite may concentrate in a limited part of the Pareto frontier preventing its complete spanning. A full view of the frontier is possible if one, first, solves the single-objective problems that correspond to the extremes of the Pareto boundary, and then uses such solutions as elite members of the initial population. The paper compares this approach with more conventional initializations by using some classical tests with a variable number of objectives and known analytical solutions. Then we show the results of the proposed algorithm in the optimization of a real-world system, contrasting its performances with those of standard packages.

Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrained many-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving convergence-hard DAS-CMOPs, while mechanisms of NSGA-II-CDP may be more effective in solving DAS-CMOPs with simultaneous diversity-, feasibility-, and convergence-hardness. Mechanisms in C-NSGA-III may be more effective in solving feasibility-hard CMaOPs, while mechanisms of C-MOEA/DD may be more effective in solving CMaOPs with convergence-hardness. In addition, none of them can solve these problems efficiently, which stimulates us to continue to develop new CMOEAs and CMaOEAs to solve the suggested DAS-CMOPs and DAS-CMaOPs.