Hsing-Chih Tsai

Modification of the fish swarm algorithm with particle swarm optimization formulation and communication behavior

The fish swarm algorithm (FSA) is a new intelligent swarm modeling approach that consists primarily of searching, swarming, and following behaviors. This paper proposes several improvements of the FSA, including: (1) using particle swarm optimization formulation to reformulate the FSA, (2) integrating communication behavior into FSA, and (3) creating formulas for major FSA parameters. This paper also focuses on studying the effects of FSA behaviors on optimization during the evolution process. Results focus on the two case study categories of function optimization (eight benchmark functions) and neural network learning (single-input single-output system identification, multi-inputs single output system identification and Iris classification problem). Evidence indicates that the proposed FSA approach reduces the effort necessary to set parameters and that the proposed communication behavior indeed improves FSA.

Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

Swarm intelligence (SI) has generated growing interest in recent decades as an algorithm replicating biological and other natural systems. Several SI algorithms have been developed that replicate the behavior of honeybees. This study integrates two of these, the artificial bee colony (ABC) and bees algorithms (BA), into a hybrid ABC-BA algorithm. In ABC-BA, an agent can perform as an ABC agent in the ABC sub-swarm and/or a BA agent in the BA sub-swarm. Therefore, the ABC and BA formulations coexist within ABC-BA. Moreover, the population sizes of the ABC and BA sub-swarms vary stochastically based on the current best fitness values obtained by the sub-swarms. This paper conducts experiments on six constrained optimization problems (COPs) with equality or inequality constraints. In addressing equality constraints, this paper proposes using these constraints to determine function variables rather than directly converting them into inequality constraints, an approach that perfectly satisfies the equality constraints. Experimental results demonstrate that the performance of the ABC-BA approximates or exceeds the winner of either ABC or BA. Therefore, the ABC-BA is recommended as an alternative to ABC and BA for handling COPs.

Predicting strengths of concrete-type specimens using hybrid multilayer perceptrons with center-unified particle swarm optimization

This study explores the effectiveness of results obtained by using proposed hybrid multilayer perceptron (HMLP) networks to predict strength in concrete cylinders, reinforced-concrete deep beams, and reinforced-concrete squat walls. Such HMLP networks were designed to incorporate one linear and three high-order layer connections. Of the latter, one, employed only in the first layer connection, was derived from drawings referenced in the literature and two were developed by the author for this study. To calculate appropriate network coefficients, this study designed a center-unified particle swarm optimization (CUPSO) approach, composed of a center particle and global and local variants, which is quite effective for optimization tasks. This study gathered 103, 62, and 62 datasets, respectively, from drawings in three cases reported in the literature. Results, which showed that certain high order HMLP models perform better than their traditional counterpart, evidence the efficacy of proposed HMLP families. Each family, comprising high-order models and a linear counterpart, achieved results that were superior to those attained using traditional MLP networks only.

Hybrid high order neural networks

Neural networks (NNs) represent a familiar artificial intelligence approach widely applied in many fields and to a wide range of issues. The back propagation network (BPN) is one of the most well-known NNs, comprising multilayer perceptrons (MLPs) with an error back propagation learning algorithm. BPN typically employs associate multiplicative weightings for layer connections. For single connections, BPN combines neuron inputs linearly to neuron outputs. In this study, the author develops and embeds high order connections (exponent multipliers) into the BPN. The resultant proposed hybrid high order neural network (HHONN) is intended to be applicable to both linear and high order connections. HHONN allows an additional connection type for BPN, which permits BPN to adapt to different scenarios. In this paper, learning equations for both weighting and high order connections are introduced in their general forms. A feedforward neural network with a topology of two hidden layers and one high order connection was developed and studied to confirm the improved performance of developed HHONN models. Case studies, including two basic tests (a function approximation and the TC problem) and squat wall strength learning, were used to verify HHONN performance. Results showed that, when the high order connection was employed anywhere except the eventual connection, HHONN delivered better results than achievable using traditional BPN. Such results show that HHONN successfully introduces high order connections into BPN.

Gravitational particle swarm

Particle swarm optimization (PSO) is inspired by social behavior of bird flocking, gravitational search algorithm (GSA) is based on the law of gravity, and both of them are related to swarm intelligence (SI). Gravitational particle swarm (GPS) is proposed where a GPS agent has attributes of GSA and PSO. GPS agents update their respective positions with PSO velocity and GSA acceleration. GPS agents, therefore, are able to exhibit PSO bird social and cognitive behaviors and motion in flight, while also reflecting the law of gravity of GSA. From results of 23 benchmark functions, GPS does significantly improve PSO and GSA, with noticeably marked improvements. This paper proposes GPS for hybridizing PSO and GSA due to the outstanding performance and interesting concepts embodied in the GPS.

Bidding Decision Making for Construction Company using a Multi-criteria Prospect Model

Abstract Two critical decisions faced by bidders in competitive bidding include, firstly, whether or not to submit a bid, and secondly (if the answer to the first is ‘yes’) what markup value should be used on the submitted bid. In the construction industry, government agencies and private sector clients typically adopt competitive bidding to determine contract awards. Contractors also apply the same approach to bidding decisions. There are many variables that affect contractor decisions regarding whether to bid and the markup scale, which complicate the bidding decision process. This study proposes a Multi-Criteria Prospect Model for Bidding Decision (BD-MCPM) to assist contractors to make decisions on bid/no bid and markup scale. Key factors of influence that impact bidding decisions were identified first. Second, Fuzzy Preference Relations (FPR) was employed to assess factor weights and determine bid/no bid. Finally, if a decision to bid is given, then the Multi-Criteria Prospect Model (MCPM), which lin...

Evolutionary fuzzy hybrid neural network for project cash flow control

This paper develops an evolutionary fuzzy hybrid neural network (EFHNN) to enhance project cash flow management. The developed EFHNN combines neural networks (NN) and high order neural networks (HONN) into a hybrid neural network (HNN), which acts as the major inference engine and operates with alternating linear and nonlinear NN layer connections. Fuzzy logic is employed to sandwich the HNN between a fuzzification and defuzzification layer. The authors developed and applied the EFHNN to sequential cash flow trend problems by fusing HNN, FL, and GA. Results show that the proposed EFHNN can be deployed effectively to sequential cash flow estimation. The performance of linear and nonlinear (high order) neuron layer connectors in the EFHNN was significantly better than the performance achieved by previous models that used singular linear NN. Trained results were used for the prediction and strategic management of project cash flow. The proposed strategy can assist project managers to control project cash flows within the banana envelope of the S-curve to enhance project success.

Using weighted genetic programming to program squat wall strengths and tune associated formulas

This study developed a weighted genetic programming (WGP) approach to study the squat wall strength. The proposed WGP evolves on genetic programming (GP), an evolutionary algorithm-based methodology that employs a binary tree topology and optimized functional operators. Weight coefficients were introduced to each GP linkage in the tree in order to create a new weighted genetic programming (WGP) approach. The proposed WGP offers two distinct advantages, including: (1) a balance of influences is struck between the two front input branches and (2) weights are incorporated throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies herein focused on a reference study of squat wall strength. Results demonstrated that the proposed WGP provides accurate results and formula outputs. This paper further utilized WGP to tune referenced formulas, which yielded a final formula that combined the positive attributes of both WGP and analytical models.

Predicting high-strength concrete parameters using weighted genetic programming

Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimized functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.

Supporting international entry decisions for construction firms using fuzzy preference relations and cumulative prospect theory

This paper emphasizes on supporting international entry decisions for construction firms in a three-phase analysis process. Phase I identified significant factors as two categories, namely country and project factor levels. Phase II stuck on identifying a risk of a country market for construction firms with fuzzy preference relations (FPR) and country factors. Of which, FPR helped elicit factor relative weights. Phase III devoted to figuring out which project or projects had the best prospects of success and profitability for assisting decisions of decision makers. Within phase III, project factors were used for project prospects, FPR for project success probability, and cumulative prospect theory (CPT) for decision maker prospects. This paper integrates FPR and CPT to select country markets and identify project prospects for decision makers of construction firms.