Conflict Objectives Research Articles

Constrained large-scale multiobjective optimization based on a competitive and cooperative swarm optimizer

Many engineering application problems can be modeled as constrained multiobjective optimization problems (CMOPs), which have attracted much attention. In solving CMOPs, existing algorithms encounter difficulties in balancing conflicting objectives and constraints. Worse still, the performance of the algorithms deteriorates drastically when the size of the decision variables scales up. To address these issues, this study proposes a competitive and cooperative swarm optimizer for large-scale CMOPs. To balance conflict objectives and constraints, a bidirectional search mechanism based on competitive and cooperative swarms is designed. It involves two swarms, approximating the true Pareto front from two directions. To enhance the search efficiency in large-scale space, we propose a fast-converging competitive swarm optimizer. Unlike existing competitive swarm optimizers, the proposed optimizer updates the velocity and position of all particles at each iteration. Additionally, to reduce the search range of the decision space, a fuzzy decision variables operator is used. Comparison experiments have been performed on test instances with 100–1000 decision variables. Experiments demonstrate the superior performance of the proposed algorithm over five peer algorithms.

Growing-up young adults and their social agency in migration: how Ukrainian children initiate and mediate their own migration within the family unit

ABSTRACT In an earlier period of migration studies, young people were overlooked, but scholars later began to study their position and perspective. Within the transnational family they are often studied as “abandoned” in the country of origin or as reunified with parent(s) in the country of immigration. Nevertheless, in both cases parents are seen as decision-makers whether young people will migrate or stay. A different angle provides studies of independent child migration or unaccompanied minors from Africa or Latin America. These young people are capable of developing their agency to the extent that they can migrate alone and neglect negotiation with parents as a result of conflict or different objectives. This article elaborates this topic and shows that growing-up adults from Western Ukraine (15–16 years-old when migrated) are also social agents, capable of triggering their own migration independently of parents as a consequence of experience from short-term stays in the country of immigration, as well as meritocratic principles and distinct generational outlooks.

Optimal load scheduling strategy for isolated building-integrated DC microgrid using binary PSO to maximize user comfort and minimize peak-to-average ratio

Due to the growth of electricity consumption in residential and commercial buildings, there is a growing burden on utilities in urban areas. Sustainable buildings are being developed by integrating renewable energy generation to make an eco-friendly environment. An isolated building-integrated (IBI) DC microgrid fed by a hybrid solar PV (photo voltaic) generator-thermo electric generator (TEG) is one viable option for obtaining self-sustainable energy for the building. The battery is an important component in such IBI DC microgrid fed by the hybrid solar PV generator-TEG for providing reliable power to the loads. However, the operation and planning of the IBI DC microgrid are challenging, and therefore it needs a suitable load scheduling strategy. This paper proposes the optimal load scheduling strategy (OLSS) under demand response to schedule the household loads in the building. The proposed OLSS aims to balance two conflictive objectives, user comfort and peak-to-average ratio (PAR), using a binary particle swarm optimization algorithm (BPSO). The proposed OLSS optimizes user comfort and PAR and reduces peak demand and battery usage. It also helps in the optimum utilization of power generated from hybrid solar PV generator-TEG by shifting the loads during sun hours. Reducing battery usage reduces the conversion loss and extends the battery life. To validate the proposed OLSS, the investigations are carried out using the data obtained across different seasons. The results show that the proposed OLSS schedules the household loads with the desired user comfort and reduces PAR by 22 % in future buildings.

Talleyrand, le droit et la mer : défendre les intérêts du commerce français dans un espace, objet et théâtre de conflits

À partir du XVIe siècle, la rivalité entre les empires coloniaux européens apporte une dimension nouvelle aux conflits interétatiques. Les puissances se disputent alors le contrôle des mers, pour des raisons économiques. Afin de saisir les ressorts de cette conflictualité on s’appuie sur l’étude de la pensée des dirigeant(e)s politiques de cette période. Or, s’il est un ministre dont l’influence est incontestable au tournant des XVIIIe et XIXe siècles, et dont l’intérêt pour les questions maritimes ne fait guère de doute, c’est bien Charles-Maurice de Talleyrand-Périgord.

Scheduling of Continuous Annealing With a Multi-Objective Differential Evolution Algorithm Based on Deep Reinforcement Learning

This paper studies a multi-objective scheduling problem in a continuous annealing operation of the steel industry, which is to simultaneously determine the production batch sizes for coils and their schedules so as to minimize the cost of setup, earliness, and tardiness. A large batch can reduce the setup costs but lead to a cost increase in earliness and tardiness. Thus, the conflict objectives of minimizing setup cost, earliness, and tardiness can be formulated separately. In this paper, we formulate a multi-objective optimization model for the problem and develop an adaptive multi-objective differential evolutionary based on deep reinforcement learning (AMODE-DRL) for effectively obtaining the Pareto solutions. In the proposed AMODE-RDL, DRL is integrated into the MODE algorithm as a controller, which can adaptively select mutation operators and parameters according to different search domains. Computational results on randomly generated instances and the practical problem instances show that DRL can effectively guide MODE to select mutation operators and parameters. The proposed algorithm can obtain better solutions compared to other powerful multi-objective evolutionary and adaptive MODE algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Setup usually leads to a decrease in production capacity and an increase in production costs. Similarly, earliness and tardiness costs are also crucial. These costs correspond to production, customer demand, and inventory, which are common objectives in production systems. However, the three objectives are generally conflicting, and it is very hard for practitioners to make appropriate decisions with manual experience. The multi-objective optimization methods can provide different scheduling for practitioners who may choose the suitable one according to current working conditions. Accordingly, the proposed model and algorithm can extend to other fields with similar characteristic problems.

A hybrid heuristic approach for the multi-objective multi depot vehicle routing problem

Efficiency in logistics is often affected by the fair distribution of the customers along the routes and the available depots for goods delivery. From this perspective, in this study, the Multi-depot Vehicle Routing Problem (MDVRP), by considering two objectives, is addressed. The two objectives in conflict for MDVRP are the distance traveled by vehicles and the standard deviation of the routes’ length. A significant standard deviation value provides a small distance traveled by vehicles, translated into unbalanced routes. We have used a weighted average objective function involving the two objectives. A Variable Neighborhood Search algorithm within a Chu-Beasley Genetic Algorithm has been proposed to solve the problem. For decision-making purposes, several values are chosen for the weight factors multiplying the terms at the objective function to build up a non-dominated front of solutions. The methodology is tested in large-size instances for the MDVRP, reporting noticeable results for managerial insights.

IMGWOFS: A Feature Selector with Trade-off between Conflict Objectives for EEG-based Emotion Recognition

IMGWOFS: A Feature Selector with Trade-off between Conflict Objectives for EEG-based Emotion Recognition

The social benefits resulting from electric vehicle smart charging balancing economy and decarbonization

Smart charging is gaining more attention with the popularity of electric vehicles (EVs). Smart charging which aims only at reducing electricity costs or reducing carbon dioxide (CO2) emissions is efficient in achieving these objectives. However, the times of lower electricity costs and CO2 emissions are not identical in power grids, putting the two objectives in conflict, which may lower the social benefits of EV smart charging. This study investigates the potential social benefit improvement of smart charging by balancing the economy and decarbonization objectives. Considering five regions located in the United States as examples, it is deduced that smart charging balancing economy and decarbonization objectives can reduce the total social costs of EV charging by 14.5%–35.1%. Furthermore, this study analyzes the impacts of three factors on the social benefits change of EV smart charging, namely charging behavior, charger power, and the social costs of CO2. In the context of transport electrification, this study provides important guidance on how to adjust the charging mode of EVs in the future to improve social benefits.

Revealing object-based cognitive control in a moving object paradigm.

Object-based attention and flexible adjustments of cognitive control based on contextual cues signaling the likelihood of distraction are well documented. However, no prior research has conclusively demonstrated that people flexibly adjust cognitive control to minimize distraction based on learned associations between task-irrelevant objects and distraction likelihood (i.e., object-based cognitive control). To fill this gap, we developed a novel paradigm during which participants responded to flanker stimuli appearing in one of multiple locations on two simultaneously presented objects. One object predicted a low likelihood of encountering an incongruent flanker stimulus and the other a high likelihood. After each response, the objects rotated clockwise such that all locations on average were 50% congruent, thereby eliminating confounds between location and likelihood of incongruence. Object-based cognitive control was evidenced by reduced flanker compatibility effects in the high compared to low conflict object. Across four experiments, we demonstrated that object-based cognitive control was dependent on a strong manipulation of the likelihood of conflict between objects and movement of the objects between trials. The novel evidence for object-based cognitive control is important in showing that people exploit not only location as a cue to guide control, but additionally objects, mirroring evidence on object and location-based attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A multi-preference-based constrained multi-objective optimization algorithm

When tackling constrained multi-objective optimization problem, evolutionary algorithms grapple with the simultaneous need to optimize the conflict objectives and satisfy constraints. The preference for the algorithm in processing objectives and constraints directly affects the feasibility, diversity and convergence of population. At the same time, the decision regarding algorithmic preference should be based on the characteristics of the current evolution. Therefore, preference decision of algorithm is a intractable challenge throughout the optimization. To address this issue, a multi-preference-based constrained multi-objective optimization algorithm is proposed in this paper, operating under the aegis of three evolutionary models. The preferences are determined by the analysis of the evolution states in concert with the actual characteristic of the population, and are implemented through the reasonable scheduling evolutionary models. When the preference changes, the algorithm identifies the useful knowledges carried by the previous populations and transfers them to the current populations. In addition, a shift-based update strategy and a new co-evolution strategy are designed for different models in the proposed algorithm, respectively. Compared to five state-of-the-art multi-objective algorithms on two benchmark suits and two real-world applications, the proposed algorithm performs better than its peers, especially in diversity difficulty and convergence difficulty multi-objective optimization problems.

A multi-objective optimization framework for functional arrangement in smart floating cities

Before the terms “smart city” and “floating city” were introduced, the world's population had increased and land shortage across the world was already widely recognized. As a first challenge, the previous studies have developed the concept of a smart city as a creative answer, following that, several scientists proposed the floating city concept in the literature as a solution to the increased sea levels. Moreover, engineers, architects, and designers deal with city planning, for smart and floating settlements as a difficult design challenge, and evolutionary algorithms could be employed to address this complex problem by optimizing residents' needs. As a continuation of our previous studies on this topic, this time, we develop a multi-objective continuous genetic algorithm with differential evolution (DE) mutation strategy (MO_CGADE) and a multi-objective ensemble differential evolution algorithm (MO_EDE) to solve the problem on hand. Then, we compare the performance of the MO_CGADE and MO_EDE algorithms with the non-dominated sorting genetic algorithm (NSGAII) to maximize two conflicted objective functions, namely, scenery, and walkability in the proposed smart floating city model created in the Grasshopper Algorithmic Modeling Environment. The parametric model that we create in the Grasshopper software includes 64 decision variables, area constraints and objective functions to be optimized by MO_CGADE, MO_EDE, and NSGAII algorithms. Computational results show that MO_CGADE and MO_EDE algorithms generate better Pareto ranking results than the traditional NSGAII algorithm in terms of cardinality, distribution spacing, and coverage metrics.

Comparative study between inerter and negative stiffness damped outrigger structures

Recently, damped outrigger structures have shown great potentials in suppressing vibrations of tall buildings subjected to seismic and wind excitations. To gain insight into the seismic performance of damped outrigger structures, this paper proposes a stochastic optimization-based sensitivity analysis procedure (SOSAP) for comparative study of damped outrigger structures implemented with inerter dampers (ID) and negative stiffness dampers (NSD) subjected to stochastic seismic excitation. The novelty of this procedure is it integrates stochastic optimization with seismic excitation modeled using the Clough-Penzien spectrum, Pareto optimal fronts dealing with conflict objectives, and sensitivity analysis of objectives values with respect to both discriminative and common design variables. First, the state-space representations of stochastic seismic excitation and structures implemented either with inerter damped outriggers (IDO) or negative stiffness damped outriggers (NSDO) are combined to yield the representations of the augmented structure-damper-earthquake systems. Pareto optimal fronts are then used to examine the tradeoffs between competing optimization objectives, which are defined as harmful interstory drift and floor absolute acceleration via the solutions of the Lyapunov equations. Subsequently, the stochastic seismic responses of the IDO and NSDO systems are compared by investigating the sensitivity of objective values with respect to inertance, negative stiffness ratio and viscous coefficient. The obtained optimal parameters of comparative IDO and NSDO systems are further examined under real earthquake records from the perspectives of dynamic response and energy balance. These investigations demonstrate the efficacy of the proposed SOSAP, reveal the comparative seismic performance between the IDO and NSDO systems dependent on the optimization objectives and the available amount of ID and NSD, which provides insight into the optimal design of tall buildings with different design strategies and conflicting objectives subjected to stochastic seismic excitation.

Balancing CO[formula omitted] emissions and economic cost in a microgrid through an energy management system using MPC and multi-objective optimization

In this work, the energy production of a microgrid is managed to satisfy the demand while simultaneously minimizing two objectives: CO2 emissions and the economic cost of operating the microgrid. To this end, a novel energy management system (EMS) that combines a Model Predictive Control (MPC), a multi-objective optimization algorithm and a decision-tool, has been developed. This EMS takes advantage of the individual strengths of these components to address the changes that frequently appear in the microgrid operating conditions. Unlike traditional optimization, MPC applies the concept of receding horizon, so that the optimization problem covers a prediction horizon instead of the entire simulation time. In addition, it is rerun at each simulation sample time with updated information, so that the controller can adapt to changes. The multi-objective optimization algorithm optimizes the CO2 emissions and the economic cost (these two objectives are in conflict objectives and need to be optimized simultaneously) and generates a set of solutions, each of which is a trade-off between the two objectives. These solutions are called Pareto optimal solutions, and they form the Pareto front. The decision-tool automates the process by managing the Pareto front obtained from the multi-objective optimization. It acts as an expert and selects, among those equally suitable solutions, the one that best fits the current priorities. To test the performance and robustness of the MPC and to demonstrate that the decreasing horizon actually helps to mitigate the uncertainties in the predictions, two simulations are performed. In the first one, the forecasting variables are assumed to be predicted without errors, while in the second one, a prediction error is added to these variables. For each experiment, the decision-tool has been adjusted to select, from the Pareto front provided by the multi-objective algorithm, different solutions satisfying various requirements.

Phraseological unit &lt;i&gt;une pomme de discorde&lt;/i&gt; in cognitive-discursive consideration (on the material of the modern French Press)

This article is focused on the functioning of the phraseological unit une pomme de discorde in French from the standpoint of the cognitive-discursive paradigm. The purpose of the work is to present the foundations of the semiosis of the considered phraseological unit as a cognitive scenario, to establish its main components and their transformations in the target areas. The main material was publications in modern French periodicals Le Monde, Le Figaro, Liberation, L’Obs, 20 minutes, Le Point, posted on the respective websites, for the period from 2018 to the first half of 2022. In the process of research, methods of observation, description, interpretation, modeling, lexicographic, linguistic, contextual, cognitive analysis were used. The study of the phraseological unit was carried out in several stages: first, its status was determined, then it was studied from the semantic-cognitive and functional-discursive aspects. It was found that une pomme de discorde belongs to the group of antique phraseological units and is a phraseological universal. Its phraseological meaning ‘a subject of dispute and disagreement’ was fixed in the French language in 1680 on the basis of a cognitive scenario dating back to the ancient Greek mythology. Four microsituations were identified in the scenario structure, but none of them is profiled completely in the target area. The transformations of the basic scenario of a dispute (conflict) affected all its main components: the sphere of manifestation, the type of conflict, its dynamics, structure (participants, their goals, the object of the conflict). Target areas – areas of conflicts manifestation, in which the baseline scenario is profiled, were grouped by territorial reference (France or other states) and by thematic areas (internal or international issues). The political, economic and social spheres are mainly represented, less often the cultural ones. By its type, the conflict has transformed from interpersonal to intergroup and interstate, from trilateral to bilateral. The object of conflict in the target areas are mainly material values, less often social and spiritual ones. In general, the objects of conflicts are things and phenomena, and only in exceptional cases − persons. The subjects of conflicts can strive both for the possession of an object or for its rejection. Thus, the phraseological unit of the French language une pomme de discorde actualizes the concepts of DISPUTE, CONFLICT and WAR. The wide use of the phraseological unit and a wide variety of areas of purpose prove the relevance of ancient images, the high viability of the phraseological unit under consideration and its high phraseological potential.

A multi-AGV fast path planning method based on improved CBS algorithm in workshops

Automatic guided vehicles (AGVs) are an important component of workshop logistics distribution systems. With the continuous increase in human cost, unmanned workshops are becoming increasingly popular, and an increasing number of AGVs are being applied to workshop logistics distribution systems. However, with the increasing number of AGVs in the workshop, planning a path quickly for each AGV is challenging, and the response time of multi-AGV fast path planning cannot meet actual requirements. To solve this problem, a multi-AGV fast path planning method based on an improved conflict-based search (ICBS) algorithm is proposed. First, the path planning problem is analyzed, and the environment expression, task, objective and constraint models are established. Subsequently, a two-layer ICBS algorithm is proposed to achieve fast path planning. At the bottom layer, an improved A* algorithm is adopted, and the selection strategy of the nodes is changed when multiple minimum values are equal. In the upper layer, the conflict number, conflict objective and conflict constraint sets are established. Three priority rules including Most Conflicts First (MCF) rule, Earliest Conflict First (ECF) rule, Single Search for Conflict Point (SSCP) rule are used. And a conflict elimination strategy is proposed. Finally, the superiority of the algorithm-solving is verified based on benchmark and actual workshop maps.

Bi-objective Pareto optimization for clustering-based hierarchical power control in a large-scale PV power plant

To assist the power grid for the real-time power balance, more and more PV power plants have participated in automatic generation control (AGC). In this process, a PV power plant should distribute the dynamic total AGC signal to all the controllable inverters. Firstly, it is difficult to satisfy multiple operation requirements for the whole PV power plant. Secondly, it easily results in a high optimization complexity due to the large amount of controllable inverters, especially for a large-scale PV power plant. To handle these two problems, a clustering-based hierarchical power control is constructed with two conflict objectives, including the AGC responding performance and the reactive power reserve. With the discrepant responding performance of different inverters, all the inverters are clustered into multiple groups by the K-medoids algorithm according to their regulation parameters. Hence, the hierarchical power control contains a top-layer control and a bottom-layer control. An efficient non-dominated sorting genetic algorithm is adopted to acquire the Pareto optimal solutions for the top-layer control, then the bottom-layer control is implemented in proportion to their regulation capacities. Finally, the case studies are carried out to verify the proposed technique for a large-scale PV power plant under different AGC signals and dynamic irradiations.

Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based Multi-Objective Evolutionary Algorithm

Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.

Performance of Six Metaheuristic Algorithms for Multi-Objective Optimization of Nonlinear Inelastic Steel Trusses

This paper presents a multi-objective optimization of steel trusses using direct analysis. The total weight and the inter-story drift or displacements of the structure were two conflict objectives, while the constraints relating to strength and serviceability load combinations were evaluated using nonlinear inelastic and nonlinear elastic analyses, respectively. Six common metaheuristic algorithms such as nondominated sorting genetic algorithm-II (NSGA-II), NSGA-III, generalized differential evolution (GDE3), PSO-based MOO using crowding, mutation, and ε-dominance (OMOPSO), improving the strength Pareto evolutionary algorithm (SPEA2), and multi-objective evolutionary algorithm based on decomposition (MOEA/D) were applied to solve the developed MOO problem. Four truss structures were studied including a planar 10-bar truss, a spatial 72-bar truss, a planar 47-bar powerline truss, and a planar 113-bar truss bridge. The numerical results showed a nonlinear relationship and inverse proportion between the two objectives. Furthermore, all six algorithms were efficient at finding feasible optimal solutions. No algorithm outperformed the others, but NSGA-II and MOEA/D seemed to be better at both searching Pareto and anchor points. MOEA/D was also more stable and yields a better solution spread. OMOPSO was also good at solution spread, but its stability was worse than MOEA/D. NSGA-III was less efficient at finding anchor points, although it can effectively search for Pareto points.

Geostrategic Significance of Energy: A Glimpse of India-Russia Relations

History bears witness to how resources have become the object of many conflicts and wars between nations. The roots of imperialism and colonialism are embedded in the need for resources. Examples of these resources are both renewable and non-renewable resources such as water, minerals, fossil fuels and natural gas. Out of all these what stands prominent is energy derived from non-renewable resources. With modernization stepping into every aspect of civilized life, energy becomes the criteria to distinguish a developed country from an underdeveloped or developing country. A country brimming with fossil fuels and other natural gases shapes the foreign policy of other nations through export or making others inescapably dependent on it. It is said, then the geostrategic importance of these energy sources increases the status and position of that nation in the global power structure. In this backdrop, the geostrategic importance of Russia in power structure has been analyzed in this paper. For the bourgeoning population and sky-rocketing energy requirements, the energy available in India has not been adequate. For this reason, since independence India has been compelled to be dependent on other energy surplus countries. India's growing and deepening relationship with a reliable Russia can be explained in this light. With exit of western investments in Russia for exploration of energy the Indian companies have stepped in for new explorations and increasing production. This mutuality has strengthened their relations despite tumults in global politics and ongoing Russia's special operation in Ukraine. This geostrategic importance of energy in the friendship between India and Russia will continue till India attains self-sufficiency in producing energy.

Punishment in Jewish Criminal Law: Its Conflicting Objectives and (in) Consistencies

Punishment in Jewish Criminal Law: Its Conflicting Objectives and (in) Consistencies