Detecting local convexity on the pareto surface

Recent regain in interest in multi‐criteria optimization approaches to provide a designer with multiple solutions to select from and support decisions has led to various methodologies to generate such solutions and possibly approximate the Pareto set. This paper introduces the notions of H‐ and w‐convexities, and develops a simple method to identify local convexity of Pareto hyper‐surfaces since their shape can dictate the choice of the method to use to obtain Pareto solutions and possibly to build an approximation of that set. The method is based on comparing the results from the weighting method to those of the Tchebycheff method at any point on the Pareto hyper‐surface. If, under some conditions, the points obtained from the two methods are identical or not, a local convexity or its lack can be assumed at that location and in its immediate neighbourhood. A numerical example is included. Copyright © 2002 John Wiley & Sons, Ltd.     

Multi-objective combinatorial optimization problems: A survey

In the last 20 years many multi-objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real-world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi-objective programming methods. The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi-criteria framework. Some conclusions are drawn and directions for future research are suggested.     

Compiling Measurement Invariant Short Scales in Cross-Cultural Personality Assessment Using Ant Colony Optimization

Examining the influence of culture on personality and its unbiased assessment is the main subject of cross-cultural personality research. Recent large-scale studies exploring personality differences across cultures share substantial methodological and psychometric shortcomings that render it difficult to differentiate between method and trait variance. One prominent example is the implicit assumption of cross-cultural measurement invariance in personality questionnaires. In the rare instances where measurement invariance across cultures was tested, scalar measurement invariance—which is required for unbiased mean-level comparisons of personality traits—did not hold. In this article, we present an item sampling procedure, ant colony optimization, which can be used to select item sets that satisfy multiple psychometric requirements including model fit, reliability, and measurement invariance. We constructed short scales of the IPIP-NEO-300 for a group of countries that are culturally similar (USA, Australia, Canada, and UK) as well as a group of countries with distinct cultures (USA, India, Singapore, and Sweden). In addition to examining factor mean differences across countries, we provide recommendations for cross-cultural research in general. From a methodological perspective, we demonstrate ant colony optimization's versatility and flexibility as an item sampling procedure to derive measurement invariant scales for cross-cultural research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

The role of auditory context in action-effect-related motor adaptation

Previous research indicates that adding auditory effects to a (silent) action can lead to substantial efficiency gains in the performance of the action, while compromising the connection between the motor and the auditory event (e.g., by removing, or by delaying the auditory effects), leads the agent to compensate for the loss of auditory feedback by executing actions in a way which increases the probability of success or enhances feedback in other modalities, thus departing from the optimal action performance. The current study explored how this motor adaptation was affected when the quality of auditory feedback was reduced by contextual factors, while keeping the physical link between the action and auditory effect intact. In two experiments, participants elicited pure tones by pinching a force sensitive resistor (FSR). In some of the conditions action-effect contingency was reduced by intermixing externally initiated tones with the self-induced ones. Pinch-force measurements indicated that action optimization was affected by contextual factors. The influence of auditory context was the most pronounced when the discrimination of self-induced and external tones was made difficult by the similarity and temporal proximity of the self-induced and external tones. In these conditions, tone eliciting actions were more forceful in comparison to conditions in which no external tones were presented, and in comparison to conditions in which the external tones were easily distinguishable from self-induced ones. This suggests that contextual factors can induce similar motor adjustments as manipulating the physical connection between the action and its sensory consequences.     

MOSA method: a tool for solving multiobjective combinatorial optimization problems

The success of modern heuristics (Simulated Annealing (S.A.), Tabu Search, Genetic Algorithms, …) in solving classical combinatorial optimization problems has drawn the attention of the research community in multicriteria methods. In fact, for large‐scale problems, the simultaneous difficulties of 𝒩𝒫‐hard complexity and of multiobjective framework make most Multiobjective Combinatorial Optimization (MOCO) problems intractable for exact methods. This paper develops the so‐called MOSA (Multiobjective Simulated Annealing) method to approximate the set of efficient solutions of a MOCO problem. Different options for the implementation are illustrated and extensive experiments prove the efficiency of the approach. Its results are compared to exact methods on bi‐objective knapsack problems. Copyright © 1999 John Wiley & Sons, Ltd.     

Approximately Efficient Solutions in Vector Optimization

An approach to approximating solutions in vector optimization is developed for vector optimization problems with arbitrary ordering cones. This paper presents a study of approximately efficient points of a given set with respect to a convex cone in an ordered Banach space. Existence results for such approximately efficient points are obtained. A domination property related to these existence results is observed and then it is proved that each element of a given set is approximated by the sum of a point in a convex cone inducing the ordering and a point in a finite set consisting of such approximately efficient points of the set.     

Multiple-objective weighting factor auxiliary optimization problems

In this paper we consider the generation of efficient solutions using weighting factor, qth-power approaches for some non-convex auxiliary function optimization forms. Algorithms are given for finding e-optimal solutions for these optimization problems.     

Choice functions in multistage optimization

This paper is devoted to the multistage optimization problem with a vector-valued gain function. The notion of optimality is determined here by the choice function. The conditions for the choice function that guarantee the applicability and efficiency of dynamic programming are found.     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

Undecidable Decisions: Rationality Limits and Decision-making Heuristics

In this article the theoretic evolution and the empirical-experimental efforts that have led to the affirmation of the bounded/procedural rationality paradigm are discussed. Moreover, the debate on supporters of the “optimization” approach and supporters of the “bounded/procedural rationality” approach is traced, highlighting the irreconcilability of these two approaches and, in retort, a solid defense against a merely “reductionist” attempt of the innovative context of the Simonian theory. Critically going over the debate on decision dynamics, it becomes clear how, due to the uncertain nature of rational processes, it is impossible to establish the decision-making best way. The imperfect character of individual choice is explained by how the decider identifies a solution that appears satisfying in that moment due to cognitive and temporal limits.